Podcasts about Agent

Listen and subscribe to Money Making Conversations on iHeartRadio, Apple Podcasts, Spotify, www.moneymakingconversations.com/subscribe/ or wherever you listen to podcasts. New Money Making Conversations episodes drop daily. I want to alert you, so you don’t miss out on expert analysis and insider perspectives from my guests who provide tips that can help you uplift the community, improve your financial planning, motivation, or advice on how to be a successful entrepreneur. Keep winning! Two-time Emmy and Three-time NAACP Image Award-winning, television Executive Producer Rushion McDonald interviewed Todd Kroupa A former firefighter turned top-producing real estate agent in Georgia. Todd explains his journey from a physically demanding fire department career to becoming a highly successful real estate broker, team leader, and luxury/equestrian property specialist. The conversation walks through: His transition from the fire service to real estate Opening and managing a 400‑agent office in Florida Relocating to Georgia and re-establishing his business How he advises both first-time homebuyers and experienced sellers Emotional decision-making in buying and selling Inspections, deal-breakers, and buyer/seller behavior Multi-generational housing trends post‑COVID Why real estate remains a wealth-building tool Advice for navigating neighborhoods, schools, and due diligence His eventual ranking as #1 single agent for Berkshire Hathaway in Georgia (2024–2025) Todd emphasizes integrity, long-term relationships, and guiding clients toward the right house — not just closing a deal. Purpose of the Interview The purpose of Todd Kroupa’s appearance is to: Share a motivational career-change story — moving from firefighter to top real estate agent. Educate listeners on the real estate process — including buying, selling, inspections, and market strategy. Give practical tips for first-time homebuyers, families, and multi-generational households. Promote best practices for choosing neighborhoods, navigating emotion in home buying, and avoiding pitfalls. Highlight Todd’s success and position him as a trusted resource for Georgia real estate clients. Key Takeaways 1. Career Transition & Motivation Todd became a firefighter in 1992, retired in 2014, and began real estate in 2002. Real estate appealed to him because it allowed him to continue helping people without the physical strain. He built and managed a 400-agent office before returning to working directly with clients — his true passion. 2. Balancing Firefighting and Real Estate He often worked both jobs full-time, with limited days off. Eventually, maintaining both became impossible: “I can’t do this anymore,” he told his wife. 3. Buyer Advice Buyers make decisions emotionally first, then logically. Within the first 3–5 minutes in a home, buyers often know if they like it. Lighting, paint color, home condition, and layout heavily influence emotional response. First-time buyers need extra guidance — like “teaching someone to drive for the first time.” 4. Seller Advice Selling isn’t just about market timing — presentation matters. Neutral paint colors and bright white lighting help increase buyer appeal. Every showing is won or lost in the first few minutes. 5. Inspections Matter — and Are Deal Breakers Top inspection walk‑aways: Mold Foundation issues Roof problemsTodd stresses that if a buyer is uncomfortable before closing, “you won’t be comfortable after you close.” 6. Emotion vs. Logic Many buyers get emotionally attached and ignore red flags. Todd’s rule: commissions should never drive decisions. 7. Multi-Generational Living Is Rising Driven by COVID, high child-care costs, rising home prices. Families are choosing: ADUs (Accessory Dwelling Units) “In-law suites” Larger family compounds 8. Real Estate as a Wealth Builder Unlike stock investments, real estate allows you to: Control, improve, alter, and live in the asset. Tax advantages like 1031 exchanges and mortgage deductions compound long-term value. 9. Don’t Buy the Most Expensive House in the Neighborhood Surrounding homes cap your resale value. You may have to wait years for nearby homes to “catch up.” 10. Neighborhood Due Diligence Realtors must avoid discrimination (Fair Housing Act). Buyers should: Visit neighborhoods at night and on weekends Speak with neighbors Review school ratings and county resources Notable Quotes (from the transcript) Career & Purpose “I love helping people. That’s why I became a fireman. Real estate was another way to help people.” “I wasn’t quite sure I wanted to manage long term… my heart was with clients.” Ethics & Commission “Commissions should never be above the people.” “If you’re focused on commissions, you need to pick a different industry.” Emotions in Home Buying “Buyers think they’re looking logically, but they’re looking emotionally first.” “Within the first 3–5 minutes, they already know if they like the home.” Inspections “If you’re not comfortable with the property now, you won’t be comfortable after you close.” Neighborhood Choice “Focus on the house, but look at the neighborhood — you can’t change your neighbors.” Wealth Building “With stocks you can’t control it, improve it, or live in it. With a home, you can.” Success & Determination “Someone told me when I moved to Georgia I wasn’t going to make it. Now I’m the number one salesperson in Georgia.” #SHMS #STRAW #BESTSupport the show: https://www.steveharveyfm.com/See omnystudio.com/listener for privacy information.

Listen and subscribe to Money Making Conversations on iHeartRadio, Apple Podcasts, Spotify, www.moneymakingconversations.com/subscribe/ or wherever you listen to podcasts. New Money Making Conversations episodes drop daily. I want to alert you, so you don’t miss out on expert analysis and insider perspectives from my guests who provide tips that can help you uplift the community, improve your financial planning, motivation, or advice on how to be a successful entrepreneur. Keep winning! Two-time Emmy and Three-time NAACP Image Award-winning, television Executive Producer Rushion McDonald interviewed Todd Kroupa A former firefighter turned top-producing real estate agent in Georgia. Todd explains his journey from a physically demanding fire department career to becoming a highly successful real estate broker, team leader, and luxury/equestrian property specialist. The conversation walks through: His transition from the fire service to real estate Opening and managing a 400‑agent office in Florida Relocating to Georgia and re-establishing his business How he advises both first-time homebuyers and experienced sellers Emotional decision-making in buying and selling Inspections, deal-breakers, and buyer/seller behavior Multi-generational housing trends post‑COVID Why real estate remains a wealth-building tool Advice for navigating neighborhoods, schools, and due diligence His eventual ranking as #1 single agent for Berkshire Hathaway in Georgia (2024–2025) Todd emphasizes integrity, long-term relationships, and guiding clients toward the right house — not just closing a deal. Purpose of the Interview The purpose of Todd Kroupa’s appearance is to: Share a motivational career-change story — moving from firefighter to top real estate agent. Educate listeners on the real estate process — including buying, selling, inspections, and market strategy. Give practical tips for first-time homebuyers, families, and multi-generational households. Promote best practices for choosing neighborhoods, navigating emotion in home buying, and avoiding pitfalls. Highlight Todd’s success and position him as a trusted resource for Georgia real estate clients. Key Takeaways 1. Career Transition & Motivation Todd became a firefighter in 1992, retired in 2014, and began real estate in 2002. Real estate appealed to him because it allowed him to continue helping people without the physical strain. He built and managed a 400-agent office before returning to working directly with clients — his true passion. 2. Balancing Firefighting and Real Estate He often worked both jobs full-time, with limited days off. Eventually, maintaining both became impossible: “I can’t do this anymore,” he told his wife. 3. Buyer Advice Buyers make decisions emotionally first, then logically. Within the first 3–5 minutes in a home, buyers often know if they like it. Lighting, paint color, home condition, and layout heavily influence emotional response. First-time buyers need extra guidance — like “teaching someone to drive for the first time.” 4. Seller Advice Selling isn’t just about market timing — presentation matters. Neutral paint colors and bright white lighting help increase buyer appeal. Every showing is won or lost in the first few minutes. 5. Inspections Matter — and Are Deal Breakers Top inspection walk‑aways: Mold Foundation issues Roof problemsTodd stresses that if a buyer is uncomfortable before closing, “you won’t be comfortable after you close.” 6. Emotion vs. Logic Many buyers get emotionally attached and ignore red flags. Todd’s rule: commissions should never drive decisions. 7. Multi-Generational Living Is Rising Driven by COVID, high child-care costs, rising home prices. Families are choosing: ADUs (Accessory Dwelling Units) “In-law suites” Larger family compounds 8. Real Estate as a Wealth Builder Unlike stock investments, real estate allows you to: Control, improve, alter, and live in the asset. Tax advantages like 1031 exchanges and mortgage deductions compound long-term value. 9. Don’t Buy the Most Expensive House in the Neighborhood Surrounding homes cap your resale value. You may have to wait years for nearby homes to “catch up.” 10. Neighborhood Due Diligence Realtors must avoid discrimination (Fair Housing Act). Buyers should: Visit neighborhoods at night and on weekends Speak with neighbors Review school ratings and county resources Notable Quotes (from the transcript) Career & Purpose “I love helping people. That’s why I became a fireman. Real estate was another way to help people.” “I wasn’t quite sure I wanted to manage long term… my heart was with clients.” Ethics & Commission “Commissions should never be above the people.” “If you’re focused on commissions, you need to pick a different industry.” Emotions in Home Buying “Buyers think they’re looking logically, but they’re looking emotionally first.” “Within the first 3–5 minutes, they already know if they like the home.” Inspections “If you’re not comfortable with the property now, you won’t be comfortable after you close.” Neighborhood Choice “Focus on the house, but look at the neighborhood — you can’t change your neighbors.” Wealth Building “With stocks you can’t control it, improve it, or live in it. With a home, you can.” Success & Determination “Someone told me when I moved to Georgia I wasn’t going to make it. Now I’m the number one salesperson in Georgia.” #SHMS #STRAW #BESTSee omnystudio.com/listener for privacy information.

Darwin Stephens shares how he transitioned from a secure Fortune 10 leadership role in corporate America into full-time luxury real estate after the COVID-19 pandemic reshaped his original business plans. He discusses building the federally trademarked Selling Dallas brand and establishing authority in the Dallas luxury market through extensive media exposure and a consultative approach tailored to high-net-worth and C-suite clients. Darwin also explains how technology like Metaology's hyper-real visualization tools is transforming real estate development by allowing investors and developers to experience projects before they're built. As a triple-licensed professional and CEO/broker of the Raddr Group, he focuses on helping clients maximize their real estate portfolios. He also previews his upcoming podcast exploring the evolving idea of the American Dream and new paths to wealth, retirement, and housing in today's economy. If you'd prefer to watch this interview, click here to view on YouTube! Darwin Stephens can be reached at (469) 756-3221 and closewithdarwin@gmail.com This episode is brought to you by Real Geeks and Courted.io.

As ChatGPT pulls back on native in-app checkout, malls becomemainstream again. Is agentic commerce ready for primetime, or are consumers seeking more analog experiences? PLUS: Dick's Sporting Goods' loyalty loop that turns steps into spending power, and a dystopian new platform that rents out humans for AI agents that can't operate in the physical world. Everything old is new again. Granny's Favorite Store Goes to TikTok Shop Key takeaways: ChatGPT is stepping back from native in-app checkout, but the commerce protocol it built with Stripe lives on 77% of shoppers prefer clicking through to a website over buying directly via AI The mall remains a societal favorite third space, even as stores become shoppable content studios (just ask John Lewis) Dick's Sporting Goods' movement-linked rewards program is quietly building one of retail's stickiest loyalty ecosystems, making it a viable competitor to AI apps "Rent-a-Human" platforms signal a strange new frontier: AI agents outsourcing tasks to people in “meatspace” In-Show Mentions: How 2,000 consumers used AI to shop Gen Z Is Going to the Mall Again — WSJ Rent-a-Human Join us at Shoptalk Spring 2026! Associated Links: Check out Future Commerce on YouTube Check out Future Commerce Plus for exclusive content and save on merch and print Subscribe to Insiders and The Senses to read more about what we are witnessing in the commerce world Listen to our other episodes of Future Commerce Have any questions or comments about the show? Let us know on futurecommerce.com, or reach out to us on Twitter, Facebook, Instagram, or LinkedIn. We love hearing from our listeners! Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Plus: Nvidia invests in Mira Murati's Thinking Machines Lab. And Airbnb hires former Uber executive as new operations chief. Julie Chang hosts. Learn more about your ad choices. Visit megaphone.fm/adchoices

If you're a software engineer right now, you likely feel like your world is changing overnight. We are writing half or less the amount of code that we wrote even a year ago, which represents a seismic, groundbreaking shift in our industry. However, the rapid introduction of new tools can slide quickly from exciting to purely chaotic, leaving you feeling like you are falling behind. In today's episode, I explore how this changes the nature of our day-to-day work, and why the key to surviving this transition is shifting your mindset from a traditional "Software Engineer" to an "Agent Manager". The Illusion of Velocity vs. Actual Chaos: While the big-picture promise of AI is that the software development pipeline will move exponentially faster, the reality on the ground often feels like unadulterated chaos. Trying to adopt every new tool while spinning up multiple agents to work on parallel tickets introduces a massive new cognitive burden. The Context-Switching Trap: Understand why parallelizing agent workflows fundamentally changes your context-switching overhead. You are no longer just reloading context to build something yourself; you are reloading it to manage, review, and validate a building agent, which rapidly drains your cognitive ability and leads to burnout. The "Agent Manager" Mindset: Treating AI as just a "smart autocomplete" while you try to do the same old job will not work. You need to start viewing your role more like assembly line or process management, focusing on facilitating the system rather than typing every line of syntax. Adopt Old-School Quality Control Tactics: Discover how traditional management methods are becoming essential for individual contributors. Just like a factory manager doesn't inspect every single item off the line, you must develop methods for spot checks, anomaly detection, and standardizing outputs to evaluate the quality and quantity of your agents' work. Shift Your Work Upfront: Recognize that your core effort must move to the specification and planning phases. Your job is increasingly about setting the context, defining the prompt, and establishing strict guardrails before the agent begins its work. Redefining Your Work in Progress (WIP): Proven principles like limiting WIP and focusing on finishing rather than starting are more important than ever to reduce cognitive burden. However, you must adapt these principles to fit a workflow where you are managing processes rather than manually coding. Episode Homework: Take a step back and ask yourself: "What is my true work in progress? Am I actually manually doing these tickets, or am I managing the processes that produce quality ticket work?".

AI isn't the future — it's the present.   In this episode, Rajeev Sajja, Chief AI Officer at BrightMLS, reveals that 61% of consumers are already using AI to evaluate agents — and nearly all of them find it helpful. He breaks down exactly how agents can show up in AI search by optimizing their bio, reviews, and content for Generative Engine Optimization (GEO).   You'll also learn practical use cases for AI in listing prep, marketing, and data analysis — plus a simple 30-day challenge to build real AI skill fast. If you're not adapting to AI, you're falling behind.  

Andrej Karpathy released autoresearch this weekend — a system where an AI agent runs experiments to improve a language model overnight, keeping what works and discarding what doesn't, while the human sleeps. The project itself is fascinating, but what's more interesting is what it shares with the Ralph Wiggum coding loop pattern and a broader shift happening across domains — from software to sales to finance — where the human's job becomes writing the strategy document and defining "better," and the agent does the iterating.Brought to you by:KPMG – Agentic AI is powering a potential $3 trillion productivity shift, and KPMG's new paper, Agentic AI Untangled, gives leaders a clear framework to decide whether to build, buy, or borrow—download it at ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.kpmg.us/Navigate⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Mercury - Modern banking for business and now personal accounts. Learn more at ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://mercury.com/personal-banking⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Rackspace Technology - Build, test and scale intelligent workloads faster with Rackspace AI Launchpad - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠http://rackspace.com/ailaunchpad⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Blitzy - Want to accelerate enterprise software development velocity by 5x? ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://blitzy.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Optimizely Agents in Action - Join the virtual event (with me!) free March 4 - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.optimizely.com/insights/agents-in-action/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠AssemblyAI - The best way to build Voice AI apps - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.assemblyai.com/brief⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠LandfallIP - AI to Navigate the Patent Process - https://landfallip.com/Robots & Pencils - Cloud-native AI solutions that power results ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://robotsandpencils.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠The Agent Readiness Audit from Superintelligent - Go to ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://besuper.ai/ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠to request your company's agent readiness score.The AI Daily Brief helps you understand the most important news and discussions in AI. Subscribe to the podcast version of The AI Daily Brief wherever you listen: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://pod.link/1680633614⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Our Newsletter is BACK: ⁠⁠⁠⁠⁠⁠⁠⁠⁠https://aidailybrief.beehiiv.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠Interested in sponsoring the show? sponsors@aidailybrief.ai

"Definitely closer." That's what Sheriff Nanos told the Today show. "Red hot." That's what retired FBI Special Agent Jennifer Coffindaffer told Newsweek. Both phrases sound like momentum. But inside an FBI investigation, those words carry a specific weight — and a specific limit.Coffindaffer spent years inside the Bureau. She knows the difference between an investigation generating activity and one generating resolution. In this conversation, she pulls apart the language being used publicly in the Nancy Guthrie case and explains what it actually reflects — and what it doesn't guarantee.The FBI's command center has relocated from Tucson to Phoenix. The task force has narrowed from hundreds of agents to a focused unit. Annie Guthrie's vehicle has been returned to the family after weeks in evidence custody. Each of those moves means something specific in investigative terms — and Coffindaffer walks through all of it.She also addresses the resource standoff directly: the United Cajun Navy submitted a 41-page operational plan — thermal drones, 25 canines, coordinated desert grid sweeps. The Sheriff hasn't approved it. Coffindaffer explains the law enforcement reasoning behind that decision — and whether that reasoning still holds the longer this case goes without an arrest.At 33 days, the family is still waiting. Here is the most candid assessment of where this investigation stands from someone who has lived the inside of cases exactly like this one.Join Our SubStack For AD-FREE ADVANCE EPISODES & EXTRAS!: https://hiddenkillers.substack.com/Want to comment and watch this podcast as a video? Check out our YouTube Channel. https://www.youtube.com/channel/UC8-vxmbhTxxG10sO1izODJg?sub_confirmation=1Instagram https://www.instagram.com/hiddenkillerspod/Facebook https://www.facebook.com/hiddenkillerspod/Tik-Tok https://www.tiktok.com/@hiddenkillerspodX Twitter https://x.com/TrueCrimePodThis publication contains commentary and opinion based on publicly available information. All individuals are presumed innocent until proven guilty in a court of law. Nothing published here should be taken as a statement of fact, health or legal advice.#NancyGuthrie #SavannahGuthrie #MissingPersons #FBIInvestigation #ArizonaMissingPerson #TrueCrime #JenniferCoffindaffer #PimaCountySheriff #HiddenKillers #KidnappingCase

Fearless Agent Coach & Founder Bob Loeffler shares his insights on Pricing Awarenesses and how they're making his Fearless Agent Coaching Students rich! Fearless Agent Coaching is the Highest Results Producing Real Estate Sales Training and Coaching Program in the Industry and we can prove it will work for you if it's a good fit! Call us today at 480-385-8810 to see if it may be  good fit for you! Telephone Prospecting for Realtors means Cold Calling, Door knocking, Calling for Sale By Owners, Calling Expired Listings, Calling your Sphere of Influence, Farming, Holding Open Houses, but Fearless Agent Coaching Students di all of these completely differently and get massively better results! Find out how! Listen in each week as Bob gives an overview and explains the big ideas behind making big money as a Fearless Agent! If you are earning less selling real estate than you wish you were, and you're open to the idea of having some help, We are here for you! You will never again be in a money making situation with a Buyer, Seller or Investor and not have the right words! You will be very confident! You will be a Fearless Agent! Call Bob anytime for more information about Fearless Agent Coaching for Agents, Fearless Agent Recruiting Training for Broker/Owners, or hiring Bob as a Speaker for your next Event! Call today 480-385-8810 - or go to https://fearlessagent.com Telephone Prospecting for Realtors means Cold Calling, Door knocking, Calling for Sale By Owners, Calling Expired Listings, Calling your Sphere of Influence, Farming, Holding Open Houses, Spin Selling, but Fearless Agent Coaching Students do all of these completely differently and get massively better results! Find out how! Are You an Owner of a Real Estate Company - need help Recruiting Producing Agents - Call today! 480-385-8810 and go to FearlessAgentRecruiting.com and watch our Recruiting Video Real Estate Coaching training Real estate training real estate coaching real estate speaker real estate coach real estate sales sales training realtor realtor training realtor coach realtor coaching realtor sales coaching realtor recruiting real estate agent real estate broker realtor prospecting real estate prospecting prospecting for listings calling expired listings calling for sale by owners realtor success Best Realtor Coach Best Real Estate Coach Spin SellingSupport the show: https://fearlessagent.comSee omnystudio.com/listener for privacy information.

Fashion and beauty instagrammer talks about how she attracts clients to her travel business and discusses a great familiarisation trip to Cancun.   Monday, 9th March, 2026

Tous les dimanches à minuit, Daniel Riolo propose une heure de show en direct avec Moundir Zoughari pour les passionnés de poker. Conseils d'un joueur professionnel, actualité, tournois... Votre rendez-vous poker, sur RMC !

OpenClaw has now been in the wild for a little over a month, and builders are starting to converge on what actually works. The early experiments are revealing that agent systems can be incredibly powerful but require deliberate design choices around task separation, coordination, security, memory, and cost management. This episode breaks down ten practical lessons emerging from the first wave of OpenClaw users, from structuring agent teams and using simple file-based orchestration to treating agents as first-class employees and designing explicit memory systems.Brought to you by:KPMG – Agentic AI is powering a potential $3 trillion productivity shift, and KPMG's new paper, Agentic AI Untangled, gives leaders a clear framework to decide whether to build, buy, or borrow—download it at ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.kpmg.us/Navigate⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Mercury - Modern banking for business and now personal accounts. Learn more at ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://mercury.com/personal-banking⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Rackspace Technology - Build, test and scale intelligent workloads faster with Rackspace AI Launchpad - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠http://rackspace.com/ailaunchpad⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Blitzy - Want to accelerate enterprise software development velocity by 5x? ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://blitzy.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Optimizely Agents in Action - Join the virtual event (with me!) free March 4 - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.optimizely.com/insights/agents-in-action/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠AssemblyAI - The best way to build Voice AI apps - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.assemblyai.com/brief⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠LandfallIP - AI to Navigate the Patent Process - https://landfallip.com/Robots & Pencils - Cloud-native AI solutions that power results ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://robotsandpencils.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠The Agent Readiness Audit from Superintelligent - Go to ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://besuper.ai/ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠to request your company's agent readiness score.The AI Daily Brief helps you understand the most important news and discussions in AI. Subscribe to the podcast version of The AI Daily Brief wherever you listen: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://pod.link/1680633614⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Our Newsletter is BACK: ⁠⁠⁠⁠⁠⁠⁠⁠https://aidailybrief.beehiiv.com/⁠⁠⁠⁠⁠⁠⁠⁠Interested in sponsoring the show? sponsors@aidailybrief.ai

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Description Stop experimenting with AI and start driving ROI. Subscribe to our Newsletter:https://theultimatepartner.com/ebook-subscribe/ Check Out UPX:https://theultimatepartner.com/experience/ In this keynote from the Ultimate Partners Winter Retreat, Nina Harding breaks down the massive shift happening in the AI landscape as customers move away from experimental pilots and demand concrete ROI and business outcomes. She emphasizes that the era of selling products and time-and-materials approaches is over, replaced by outcome-based, verticalized selling where vendors and partners share accountability. Through real-world examples in healthcare and retail, Harding outlines how partners can leverage Copilot Studio, Agent 365, and Microsoft’s incentive programs to build specific superpowers, differentiate themselves, and ultimately lead the AI mission alongside Microsoft. Key Takeaways Customers are no longer interested in AI experimentation and now expect immediate, concrete return on investment. Selling products is dead; the modern approach requires a consultative, signal-based strategy focused entirely on business outcomes. The traditional time-and-materials billing model is disappearing as clients demand shared accountability for project success. Rapid proliferation of AI agents has made security and governance top priorities for enterprise customers. Success in the Microsoft ecosystem now requires partners to highly verticalize their value propositions by industry. Defining and clearly articulating your unique “superpower” or niche is essential to stand out to the Microsoft field sales organization. https://www.youtube.com/watch?v=HJJ4Zcf4tZc&t=1920s If you're ready to lead through change, elevate your business, and achieve extraordinary outcomes through the power of partnership—this is your community. At Ultimate Partner® we want leaders like you to join us in the Ultimate Partner Experience – where transformation begins. Key Tags Nina Harding, Microsoft AI, artificial intelligence ROI, AI agents, Agent 365, Copilot Studio, outcome-based selling, verticalization, healthcare AI, retail AI, Cognizant, Davos 2026, AI governance, AI security, technology transformation, Ultimate Partner Live, enterprise AI adoption, digital transformation, system integrators, AI pilots Transcript [00:00:00] Nina Harding: More importantly, we want to serve more and more people faster, and AI is coming in and having a very practical approach in healthcare alone. [00:00:14] Vince Menzione: We just finished Ultimate Partners Winter Retreat here in beautiful Boca to a sold out [00:00:19] Vince Menzione: crowd. Come join me now for a compelling discussion on the impacts of the tectonic shifts we’re all seeing. [00:00:27] Vince Menzione: I feel incredibly fortunate, uh, to have this, this, this friend Nina who came into the studio here for the first time, actually earlier, well last year, geez, earlier this year. [00:00:38] Vince Menzione: It was last year, right after my accident I think. And, uh, we gotta spend some time together. And she was so good to, uh, make her time available and her team’s time available to come down here to be with us today. Ne I’m so thrilled to have you. I am going to turn over the stage to you. Uh, you’ve got some incredible learnings. [00:00:57] Vince Menzione: I know you’ve been on the AI tour with Microsoft. Yeah. And you’ve got some great learnings you’re gonna share about what’s happening. Absolutely. So it’s so great to have you. [00:01:05] Vince Menzione: It’s nice to see you. [00:01:06] Nina Harding: Nice to see you. [00:01:07] Nina Harding: Thank you. Well, thanks everyone. It’s great to see so many familiar faces and then some new faces as well. [00:01:15] Nina Harding: Um, because we’re in a little bit more of an intimate environment, I thought I would approach this a little bit differently. Give you some better insights into what we’re actually hearing at Microsoft with our customers, some of the things that are actually moving the needle that we’re seeing some of our partners do. [00:01:34] Nina Harding: So really to share some of the best practices out there, and hopefully you’ll leave with some more insight or tips and tricks, um, is really what I would love to do because our job. Collectively is really this transformation and to take a advantage of it out there in the market right now. [00:01:57] Nina Harding: Let’s see [00:01:57] Nina Harding: here. [00:01:59] Nina Harding: I can move slides. Well, this one isn’t moving. Any slides? [00:02:07] Nina Harding: No. Okay, great. So, um, some of you might. Uh, know that I’m a Floridian now, right? So I just live right up, up the way in Palm Beach. Um, so not too far, but I still wouldn’t miss this opportunity to be with all of you. Um, there is an energy that I think that we’re all feeling right now, and, uh, it’s, it’s palpable. [00:02:32] Nina Harding: We’re finding right now that our customers are really going from this landscape of experimenting with ai. Really to looking at the outcomes and having expectations around the momentum that they’re seeing. Right. That’s a big shift, right? We, and things are going pretty quickly, so I look at things almost quarterly now on what is that core message and what are, what is the difference in the tone from our customers of what they’re expecting? [00:03:06] Nina Harding: What we’re gonna talk a little bit about today is how all of you, our partners, are such a critical part of that journey. Actually, sometimes the most important part. You’re on the front lines with the customers. You’re the ones having those conversations. You’re the ones that are in there arm to arm with their teams, listening to what they’re experiencing, their challenges that they’re facing, and they’re really wanting now to go from this world of, Hey, we have lots of different pilots. [00:03:41] Nina Harding: Right? A lot of us know that right into, oh my gosh, it’s not about pilots anymore. They really want that ROI story. They want those outcomes and it’s looking very different for all of us. The way that we sell, the way that we go into our engagements, the way that we even price things, the way that we, meaning Microsoft partner and customer are locking arms is fundamentally very different. [00:04:15] Nina Harding: We have to go in collectively. We have to also be responsible for the outcomes and deliver on those. ROI is that headline that we’re all after. Right. It is the most important part of the puzzle right now because there isn’t a single boardroom that isn’t talking about AI and you guys are all experiencing it. [00:04:39] Nina Harding: It’s easier than ever to go in and have the conversation. The hardest part is how do we quickly get to an ROI study, so you or ROI case so that we can continue to build on that. And when you’re looking at this every. Customer is providing signals out there to help you grow that penetration into the account. [00:05:04] Nina Harding: And I’m gonna share some of the signals that I think that are really meaningful. But that’s the most important thing is we’re no longer, and I know you guys all know this, we’re no longer selling product at all anymore. We’re selling those outcomes. And I can tell you at Microsoft, we’re spending a tremendous amount of time retraining all of our sales reps. [00:05:25] Nina Harding: Really to be focused on how do you listen and do that consultative signal based sale. How do you actually go in and start selling, not selling, but I mean it is selling, but listening to the journey that they want to go through. What are the challenges that they’re facing and what’s the transformation that we’re able to kind of go and be a part of together with our partners? [00:05:54] Nina Harding: Notice it’s not about product. Product is just the tools in your tool chest to create those outcomes. So that’s gonna be really important as we go through this journey. [00:06:09] Nina Harding: Uh, so I saw the, the title of the session, uh, mentioned Davos and Davos was an interesting time. Uh, Microsoft has a very, actually, a very big presence at Davos and, uh, we had over 300 customer meetings there, uh, where we were meeting with some of the top companies around the globe. And it was very much affirmed that. [00:06:34] Nina Harding: Uh, the, the concept of AI we’re past, like curiosity stage, right? We’re way past that and we’re even past that. The art of the possible discussion, right? Uh, what the, the customers are almost at the point is, is come in and tell me, tell me what to do. Show me how to do it. It’s a very different position than, Hey, we’re presenting you with all these different possibilities. [00:07:08] Nina Harding: They’re They’re tired. They’re tired of all the possibilities. They wanna get to the brass tacks of how are you gonna change my customer service department? How are you gonna make it easier for my hr? How am I going to derive growth? What are some of the other things that you guys are experiencing out there? [00:07:23] Nina Harding: Like what are some of those other ROI drivers that people are asking, where am I gonna find the money? What for? For doing the project or out of the project? Other people? I Okay. To do the project. Okay. Resourcing. Okay. So what we’re seeing here is that, uh, the conversation is very much now focused on, okay, I need sec, I need security. [00:07:50] Nina Harding: That has been louder than ever before. So, Vince, the one thing I would say about that slide where you had those five different pillars, I’d put security on the bottom. Understanding your data, your data platform on the bottom, those are consistent across all those pillars. And then you can kind of hit at them. [00:08:10] Nina Harding: But, uh, there’s a lot of energy, there’s a lot of excitement, but it’s rooted in what are you materially going to do to change my business, and is your skin in the game to help me do it and I’ll pay you for that outcome? The concept of this time and materials approach gone. Gone. Even at Microsoft, we’re adjusting to the fact that the customers aren’t like, oh. [00:08:35] Nina Harding: Just hand it over to a system integrator and they’ll deliver on it. They’re like, oh no, we want you accountable too. You’re accountable for the outcomes as well, which is, oh gosh, okay. How do we do that in a partnery model that makes sense where we’re not tripping over each other, but we’re going in stronger together. [00:08:54] Nina Harding: We have one message together and we’re really focused on driving that. They’re also really concerned around the governance of all these agents, right? I see a lot of heads shaking on this. I mean, there’s a lot of proliferation right now. There’s a lot of excitement. I mean, I don’t know in your companies, but people are building agents faster and quicker, uh, than ever before, and some of them are really, really cool and they’re making huge point savings of times. [00:09:22] Nina Harding: Everything from. You know, some of you guys have probably heard me talk about everything from, uh, working on performance reviews to what are all of the incentives that we have for partners and making that easy to understand to, uh, to helping me understand patterns in our financials and what partners are really performing and growing. [00:09:45] Nina Harding: All of these agents are just popping up everywhere, but that creates a real governance issue and a real security issue for a lot of companies as well. So you take all of this and you hear this momentum and I think, uh, that together we’re really well poised. I think Microsoft is in a unique position together with you. [00:10:07] Nina Harding: On this frame, we have Agent 365, which helps you manage all these different agents, right? So that’s an exciting. How many of you’re familiar with agents? 365. Great. And I promise I’m not a product person. I’m not gonna do a lot of pitches, so don’t worry about that, um, at all. But, uh, we also have copilot studio and foundry, and so we have this whole, uh, set of capability, but that capability only comes to life if we’re able to connect with the customer, build the outcome, and making sure that the CEOs see all of us as their partners on that strategy and journey. [00:10:47] Nina Harding: So what does that look like? So I talked a little bit about signals, and signals, is that ability to listen to the, to the customers, what’s really, really me, uh, meaningful and frontier firms are doing this on a consistent basis all the time. Listening to the specific needs use cases, et cetera. So we at Microsoft have been trying to not only share all these different use cases that we have exposure to, but in addition. [00:11:17] Nina Harding: We turned on functionality, and I’ll talk about that in a little bit so that we can also share amongst each other as a community and understand those use cases. Uh, what’s really important is that, um, we’re moving from this world of all these like little one-off projects to a strategy and a platform that everyone wants to move to, but it’s all also getting powered by agents. [00:11:42] Nina Harding: That’s, that’s where we are today. So. [00:11:49] Nina Harding: Having a little trouble. I’m not gonna go through this too. Everyone’s familiar with this in, in here, the Frontier overview. If you’re not, let me know. Um, but basically one of the things that we find is really helpful is, is just sharing where we have seen proof behind having the conversation around the AI journey. [00:12:12] Nina Harding: Around the, the customer journey as you’re going out there. Um, there are really four different areas that we’ve talked about, and I’m not going to drain this ’cause there’s lots and you can, you can, uh, go onto the internet. You can see me talking about all these different areas. I don’t wanna spend too much time here, but these are four of the different. [00:12:33] Nina Harding: I would say categories where when you’re looking at different ways that you can make a material difference with the, the, the customer that we find the most momentum. So around enriching employee experiences, changing the way we, uh, engage with customers. Uh, changing processes as well. And then, uh, the outcomes, like really transforming the way we go about business. [00:12:59] Nina Harding: And we wanna do something about bringing it in to the flow of the work, everyday work. How many of you are finding that you’re actually using agents in your day-to-day workflow? Isn’t that cool? And then as you continue to use it, it becomes easier and easier and easier. And. I know from my team, I’m starting to look at what is the e everyday usage versus the monthly usage, right? [00:13:26] Nina Harding: It’s the every day. It’s become almost, uh, your second hand. And what’s important, uh, on this is that we’re giving, uh, listening to all these signals giving, um, the consistency, um, of the, the engagement with. With the clients, we’re able to all share the same stories and be able to scale at a much faster pace. [00:13:54] Nina Harding: So what does that look like? Here we go. Um, one of the things that we talk about at Microsoft, and the reason why I have this up here is that we’ve moved the conversation away from product into these customer outcomes, which really becomes about. Industry discussion. You have to speak their voice. You have to understand their business problems. [00:14:21] Nina Harding: You have to listen for what is materially different. So I’m actually sharing this, which you don’t normally see in a lot of presentations out to Microsoft about the structure of the organization, the takeaway. This is a sales organization in enterprise. The takeaway that I want you to have from that is look at the verticalization. [00:14:43] Nina Harding: We’ve done. It’s no longer by territory. The ball has moved, the conversation has moved entirely. So what does that say to all of you as well? Your value proposition as you’re working with our field has to be verticalized. The way you engage has to be verticalized. What you say, um, what the, the outcomes that you think differentiates yourself. [00:15:12] Nina Harding: Verticalized. So there isn’t the approach of like doing this like mask gorilla campaign across, for example, the Americas. And I’m just using this as an example on, um, the small and medium business side as well. Um, the, they’re a little bit more territory based still, but um, at least at the enterprise, everything has to be about customer value. [00:15:38] Nina Harding: Customer value. So, um, what this also suggests to me is the way we’re working and where we’ve seen a lot of success is when all of you are starting to tailor your messages and differentiate yourselves by customer success stories. Use cases where you’ve had premise, uh, penetration as a software partner, but you have to tie it back to the industry again. [00:16:05] Nina Harding: It’s just different. And so if I’m very transparent that that’s become, has gone from a nice to have to critical as the field is looking at, who are those go-to partners? It’s the go-to partners that speak retail. It’s the go-to partners that speak oil and gas and I don’t know, I, I, I see some nodding of heads. [00:16:27] Nina Harding: Some people know this, some people don’t. But I can see the shift tremendously over the last six months. So, um, hopefully that’s helpful in, in, in kind of sharing just how we’re walking the walk and talking the talk. So as I go back to industry, um, I thought what would be helpful is to take a few examples so you have a chance to see. [00:16:52] Nina Harding: In life, what are, what are we actually seeing at Microsoft? And if you guys are seeing something else, I would love to hear that too. But these, this is an example in healthcare and when we’re looking at, uh, a particular industry, we’re looking at what are some of the pain points? What are the top trends? [00:17:11] Nina Harding: What are some of the challenges folks are, are facing? And then what are the use cases that are really making traction here? This is a different way of taking that frontier vision and doing that click down by industry. And so what we’re also doing is we’re looking at who are partners that can help us in healthcare that can help answer some of these key challenges. [00:17:35] Nina Harding: Who are the ones that have the ability to have those material conversations in that trust? In healthcare, for example, there’s a ton of pressure. I mean. We all are consumers of healthcare. Hopefully we, all of us, have been lucky enough to have healthcare, um, in the, in this, uh, forum, but there’s a lot of clinician burnout, rising costs, right? [00:18:01] Nina Harding: The, the expense for, uh, medicines and so forth. But more importantly, we want to serve more and more people faster, and AI is coming in and having a very practical approach. Healthcare alone. So many of you, I talk about, um, the fact that at one point I was paralyzed, right? So I was paralyzed from T two down and, um, I go in every six months for an MRI, uh, to check, to check if everything’s still functioning. [00:18:32] Nina Harding: And the nervous system is going well. My doctor has had to manually look at that. Now he’s using AI to look at. History and the progression since 2008. That’s game changing. And on top of that, he is looking at me and having a conversation and looking in my eyes and observing me instead and using Dragon to have it feel epic to really think about how that’s changed my personal experience with the healthcare system and changed how a physician can show up. [00:19:09] Nina Harding: So there are many, many, um, many use cases around like patient access and, uh, innovation that we’re trying to do, surgeries, uh, being able to do clinical, clinical trials, but AI is everywhere and that’s what’s really important is that we’re figuring out for all of you what your software solution. Services offering, or even if you’re selling that, you have that value, value proposition down at that level. [00:19:43] Nina Harding: So let’s take a look at retail, for example. We have a short little video. Are we gonna be able to run that video? This is where we’re seeing a lot of shrinking. Margins, people wanting more, uh, intimacy with their customer. Here we go. [00:21:09] Nina Harding: Are we good? Well, that was a quite, uh, quite a nice, uh, uh, digital response to the end of the video. But what you’re seeing is people are using it in all different facets as we go into an example. I always love to do, use examples of partners that are hitting the mark ’cause we can all learn from ’em and myself included. [00:21:30] Nina Harding: We’re partners that are really successful. I chose to use Cognizant. Cognizant was actually our partner Si of the year, um, at the Americas level. And one of the things, and I won’t drain it on, um, the right hand side of this, uh, the slide, but they really are helping the customer’s move in a framework approach by industry, uh, to an AI landscape. [00:21:58] Nina Harding: Uh, they, they have secured an end-to-end solution and they’re focused on real business outcomes, and they have been growing at over 30% year over year. Huge. That’s great. Right? That’s what we all want for our businesses. And so what you’re seeing here is. They have a narrative around the frontier firms and they pull that through when they’re engaged in the clients and with our field. [00:22:27] Nina Harding: And then they’re using the incentives that we have. And don’t worry, I have a slide on some of the incentives we have, um, to actually make sure that they’re using those effectively in the pre-sales motion, but most importantly on the adoption and the change management after they’ve actually, uh, built out the solutions. [00:22:45] Nina Harding: And that’s really, really, really key here. So here’s an example of, um, of Cognizant at Coldwater Creek and Soft Surroundings. They had two different platforms and they brought it all together and then they brought Dynamics in as well. And what they have actually been able to do is improve a lot of the inventory management, the visualization, um, of all the inventory around. [00:23:14] Nina Harding: Around all of their stores and their warehouses, and they’ve been able to streamline the fulfillment and improved, uh, reduced back orders. What you’re seeing is those are all concrete examples of the outcomes that they were trying to drive for at the beginning, and those were all. Key pain points. And so they go in, cognizant will go in and understand with what are the material things that you are, that’s keeping you up at night, that is creating that drainage, uh, in your accounts or if you could transform, what does that look like? [00:23:52] Nina Harding: And so there, they spend the whole conversation together with Microsoft focused on doing that. And then we do the outcome based proposal. Very different, right? It creates for a much stronger vendor relationship, and the customer feels like they really have in the essence of the word partners, helping them to be successful. [00:24:15] Nina Harding: Right. [00:24:20] Nina Harding: Here we go. So I promised you some of the incentives, and I know you might just take a, a quick peek at some of these. These are, these are, um, some of the incentives that. Microsoft has put forward to help our partners on this journey. Uh, this is a slide that we’ve created from the America’s perspective to try and simplify it. [00:24:42] Nina Harding: Now there’s a lot behind it, right? But to try and help simplify, um, where are the incentives available? And I think this is one of the first times you’re actually saying what’s available for the sis. Versus for the software partners. And then we’re gonna hear more today about what’s also available for the channel partners as well. [00:25:03] Nina Harding: Um, it’s really thinking about what is your behavior as a partner? How are you showing up? How are, uh, you making a contribution to that customer? And then how can Microsoft best support you in that journey? So there’s all sorts of, uh, all sorts of incentives here, and it’s really, uh, designed to be flexible to what you need. [00:25:24] Nina Harding: But for the, I, I think it’s very focused on the value proposition as well that you bring to the table. So, um, I encourage you to take a look at this, make sure that you have this in your diary or your flipping of, of how are we maximizing, um, deals. And we can certainly go through a lot more of this. And we have webinars and so forth that will take you through all of that. [00:25:52] Nina Harding: Alright, so. I’ve talked a lot about this outcome-based selling, and that’s, it’s literally how Microsoft is starting to move forward on how do we go about engaging with the customers and with our partners. You’re gonna see, because our customers are asking more Microsoft involved and for us to go jointly into the opportunities. [00:26:16] Nina Harding: Not that we necessarily, we’re not building out a larger consulting force or anything like that, but. We want to make sure that the customer ask that Microsoft is engaged in working with our partners, is honored, um, and that we’re, we’re part of that, and that we’re also sharing our, our experiences and learning from all of you at the same time on who has the best, uh, approach, Beth best, best methodologies and best practices to light up our customers together. [00:26:51] Nina Harding: But the ROI doesn’t really show up just in dollars alone. We all know this, right? Um, it could be in, uh. Satisfaction it could be in care. So as you’re starting to look at this new evolution of how we’re really landing the value proposition of ai, we have to think outside of the box that it’s not just monetary and it’s not, I think you said savings or securing funds and so forth, but it’s really of how do I leapfrog into the modern world? [00:27:22] Nina Harding: How do I change that entire experience and think outside of the box? And, uh, make sure that the conversation is not just about how do we optimize certain practices, but how do we have this more executive level strategy conversation on the future of how we’re gonna engage with our clients, uh, their clients in a much more, um, I think transformative and personal [00:27:51] Nina Harding: way as we go forward. [00:27:54] Nina Harding: So we know that if the outcomes are the, what we’re looking to go drive, the next question is really how do we go do that? And that is gonna be through the agents on here. You’ll see just from from out in the market, what we see will light up the market. We think that, or I can’t even say we, IIDC says 81% of leaders are expecting agents. [00:28:24] Nina Harding: Full utilization in the next 12 to 18 months. And to be honest, I think this quote is probably even two months old. So we’re already, we’re probably down to like, you know, eight, eight to 12 months. And what I’m seeing that proliferation happening, it’s crazy. So understanding that value proposition, um, whether you’re from a software company or a services company or even some of our resellers, what’s that niche? [00:28:52] Nina Harding: What’s that industry or sub-industry? What is that? Horizontal. I go after customer service within, uh, the manufacturing vertical. Right. And then are you building out agents or do you have capability? And that’s what we’re doing internally at Microsoft as well, is to help make that really visible to the field so that you’re differentiated. [00:29:15] Nina Harding: Differentiation is gonna be really key right now because there’s so many people that say, oh, I do migration services, or I can help with data, or I can do security. But it’s the specificity around the industry and what you are truly known for within that space. So one of the things that we look to do is, is looking at all of the different areas where we see agents popping up. [00:29:44] Nina Harding: And this is a helpful slide. Sometimes I think, um, it starts to highlight, um, where we’re seeing some traction in financial services. Or in healthcare manufacturing. And then when I talk about the horizontals or the personas, you start to see some of the um, really repeatable, high return on investment type of things. [00:30:08] Nina Harding: Is this resonating with some of you guys? Yeah. I’m seeing a hit, a lot of head nods. This, if you’re on the services side, right? We’re in an intimate setting. This is where I encourage you to try and build an agent, right? Package that agent, put it on marketplace, make that available, and then make that known to our field sales organization. [00:30:27] Nina Harding: ’cause they are looking for quick wins along those lines. [00:30:31] Nina Harding: So on that, um, [00:30:36] Nina Harding: uh, one of the things that we’re along the journey for is the skilling. This is moving at such a fast pace, right? Um, so you’re looking at. Um, anthropic is really a big topic right now, right? Gemini, you’re looking at cloud, you’re, um, or Claude. [00:30:55] Nina Harding: Um, you’re looking at all of these different, uh, scenarios and one of the things at Microsoft is we really wanna be open to all of these different technologies because our customers are open. So we want to be part of taking you on that journey. And one of the things that we invest in white. [00:31:12] Nina Harding: Significantly is all of the training. Um, and I wanna encourage you guys to take advantage of it. Training is not a one-time thing. It is, it is a constant muscle that you must exercise. So as I come to my conclusion, I have a couple three key things, right? One is really understanding what your superpower is, right? [00:31:33] Nina Harding: The partners that I’m finding are really aligned well with the field are really winning. Those stories are the ones that have. Know and can articulate their superpowers. What am I known for? What are the use cases I can either build to or have agents against? And where have I done this consistently? And packaged really, really concretely, right? [00:31:55] Nina Harding: Um, this, this proliferate of like, I can do everything. Unfortunately, you get lost a little bit in the noise, right? So clear positioning, proof point’s, so critical right now, and reinforcing that credibility with the clients that have adopted. The second thing is that you’ve heard a little bit about this hopefully. [00:32:16] Nina Harding: How many of you have heard of the part partner success story? Okay, this is really, really key. We launched about maybe a month ago, and we already have over a hundred, uh, stories from partners, and the field is loving it. What it is is it brands the stories with your brand if you submit them. So what? Talk about credibility, um, with the field and with our marketers to have your name and that recognition picked up. [00:32:45] Nina Harding: It’s really, really fantastic. So I encourage you to do that. For those of you taking quick snaps, I did put a code on here, so if you wanna go straight to it, uh, you can take it. Um, and go explore with it. What’s nice about it is it’s AI based, so it will help you write these stories very, very quickly. [00:33:04] Nina Harding: There’s no reason why your sales reps can’t be writing these stories, and then yes, [00:33:11] Nina Harding: uh, yeah, you can do no meaning like from enterprise. No. Mm-hmm. Mm-hmm. You can do it on any, on any, there is a different level of fidelity of if you have the customer’s permission. Right. Um, to pu to publish it or not. And that’s some functionality we’re working on. If there’s enough traction of, of this is to help you guys. [00:33:32] Nina Harding: Secure that with Microsoft. Yeah. Um, but yeah, it can be any customer there. But I encourage you to take a look at that. And I know I’m two minutes over here, so I’m just gonna leave you with this. Um, at the end of the day, as I, as I wrap up here, I just wanna make sure that what, where we’re going and we’re going together, that it’s simple and actionable between us and it’s easy for our field to understand. [00:34:00] Nina Harding: Where you play the value proposition you play so that we’re going into deals even more effectively together. Right? So you heard industry, sub-industry, persona level or horizontal. Put that in if, um. Figuring out what your superpower is, making sure that you’re trained, that there’s evidence around the success, and capturing that in ways, uh, that are critical to not only your business, but giving us the visibility of that success. [00:34:31] Nina Harding: Like scream from the rack rafters. Use these tools to make sure that we know just how transformational you’ve been in some of the customers and where you’re uniquely winning. So, so important. So keep investing in the skilling. You can see my kind of like five power plays, right? And the last one always being that superpowers. [00:34:56] Nina Harding: So with that, um, if we do all of these things consistently, you won’t just be keeping up with ai. I think we will all be leading on that AI mission. So thank you very much. I appreciate it. [00:35:14] Vince Menzione: Don’t forget, ultimate Partner Live is coming soon, May 11th through the 13th in beautiful Bellevue, Washington. I hope to see you there.

Recorded 2026-03-08 22:01:21

如果你最近发现朋友圈里的科技大牛们开始讨论“养龙虾”，甚至连二手市场的 Mac mini 都变得一机难求，请不要惊讶 。这并不是某种新型水产养殖业的复兴，而是一场名为 OpenClaw 的通用智能体正在破圈 。在这期节目里，我们试图剥开这个被戏称为“小龙虾”的 AI 工具那层神秘的外壳：它为什么能让一个奥地利程序员在待业期间随手写出的代码，演变成让全球极客疯狂的生产力利器 ？从本质上讲，OpenClaw 正在重新定义我们与 AI 的协作方式。过去我们习惯于在一个对话框里小心翼翼地推敲提示词，指望 AI 能给出一个完美的文案；而现在的“养虾人”更倾向于给 AI 一个模糊的目标，让它像个真正的私人助理一样，跨越平台去查机票、定餐厅，甚至在遇到阻碍时自己注册虚拟电话去和人类沟通 。这种从“聊天”到“执行”的范式转移，正是 Mac mini 这种高性价比算力中心被抢购一空的根本原因 。然而，这种近乎“贾维斯”般的体验并非没有代价。当一个 AI 拥有了操作你电脑、读取你邮件、甚至动用你信用卡的权限时，安全边界该如何划定 ？在这场全网参与的“养虾”实验中，我们不仅聊到了最硬核的避坑指南和模型选型建议，更深入探讨了这种不确定性带来的惊喜与风险。无论你是一个想给公司省下十个员工成本的初创老板，还是只想让 AI 帮你在早晨汇总新闻的普通用户，这期节目都能让你在喧嚣的“养虾”热潮中，看清 AI 时代的下一个入口。【本期知识要点】走进“小龙虾”：它到底是什么？00:03:05 什么是 OpenClaw？ 名字的由来及其诞生的背景00:05:19 核心功能拆解： 相比普通 AI 对话框，它多了“消息系统”和“任务系统”00:07:43 通用智能体（Agent）的概念： 它更像是一个“会思考的操作系统”，能自主想办法达成目标00:12:10 什么是 Skills（技能）？ 相当于“手机 APP”，是模型与物理世界能力连接的桥梁行业现象：为什么突然火出圈？00:18:37 火爆诱因： 傅盛等自媒体博主的推流，加上公众对 AI “从聊天到干活”的焦虑感与期待00:20:38 大厂推波助澜： 阿里云、腾讯云、百度等迅速推出云端一键部署服务00:21:34 催生“第三产业”： 衍生出 500 元一次的“带装龙虾”上门安装服务及代养、代修业务硬件与环境：怎么装最靠谱？00:24:01 “矿场”新形态： 为什么 Mac mini 会全网断货？解析 M 系列芯片在算力上的优势00:28:38 安装方案对比：* 本地物理机： 权限最高但也最危险虚拟机/Docker： 安全隔离，但操作桌面端应用受限云服务器（VPS）： 腾讯云/阿里云的轻量服务器方案，适合小白低成本体验00:53:03 安全预警： 警惕恶意 Skills 窃取私钥或删除本地文件，强调“信任边界”的重要性进阶指南：如何养出一只“聪明”的龙虾？00:54:38 接入交互工具： 对比 Telegram、飞书、企业微信、QQ 等工具的对接优劣00:58:02 大模型的选择：* 国外模型：Claude 3.6/GPT 等能力最强，泛化与收敛能力更好国内模型： MiniMax（高性价比）、智谱 GLM 5、DeepSeek（OCR 强项）01:14:11 省钱技巧（Token 避坑）： 避免闲聊，直奔主题，理解模型自主思考带来的“Token 燃烧”场景应用：它能帮你做什么？01:18:23 职场助理： 自动监控航班动态、整理会议纪要、抓取每日新闻01:21:42 投资参考： 辅助分析股票基金基本面01:24:20 自媒体辅助： 批量生成选题与图文的边界与局限性01:30:31 高级玩法： 结合安卓短信转发器，让 AI 掌握个人实时信息辅助决策常见问题 （FAQ）【相关节目】科技乱炖：当可靠的代码变成了偶尔发疯的OpenClaw，我们未来的工作范式变迁【本期主播及嘉宾】朱峰：「津津乐道播客网络」创始人，产品及技术专家。姝琦：「津津乐道播客」&「不叁不肆」主播，互联网大龄产品经理（微博：@姝琦_HU，小红书：@姝琦同学）【制作团队】后期 / 声湃剪辑大模型封面 / 姝琦监制 / 姝琦运营 / 卷圈产品统筹 / bobo【联系我们】希望大家在听友群和评论区多多反馈收听感受，这对我们来说十分重要。欢迎添加津津乐道小助手微信：dao160301，加入听友群【关于「津津乐道播客网络」】在一派纷繁芜杂里，我们为愉悦双耳而生。科技、教育、文化、美食、生活、技能、情绪……严肃认真却不刻板，拒绝空泛浮夸。与专业且有趣的人携手缔造清流，分享经历，传播体验，厘清世界与你的关系。津津乐道 | 科技乱炖 | 津津有味 | 记者下班 | 不叁不肆 | 厂长来了 | 编码人声 | 沸腾客厅 | 拼娃时代收听平台苹果播客 | 小宇宙App | 汽水儿App | Spotify | 喜马拉雅 | 网易云音乐 | QQ音乐 | 微信听书 | 荔枝FM | 央广云听 | 听听FM | Sure竖耳App | Bilibili | YouTube联系我们津津乐道播客官网 | 公众号：津津乐道播客 | 微信：dao160301 | 微博：津津乐道播客 | 商业合作：hi@dao.fm | 版权声明 | RSS订阅本节目由「声湃 WavPub」提供内容托管和数据服务支持。

Jesse Genet shares how she built a team of AI agents to transform homeschooling, family life, and personal productivity without a software background. She explains how agents like an AI chief of staff, curriculum planner, and content creator help design personalized lessons, analyze kids' learning, manage educational toys, and even run TikTok. The conversation covers practical delegation workflows, guardrails and trust, and why she treats AIs like employees with onboarding and clear roles. Jesse also explores local models, privacy, and how AI in the home could reshape future work and family life. Use the Granola Recipe Nathan relies on to identify blind spots across conversations, AI research, and decisions: Sponsors: VCX: VCX, by Fundrise, is the public ticker for private tech, giving everyday investors access to high-growth private companies in AI, space, defense tech, and more. Learn how to invest at https://getvcx.com Claude: Claude is the AI collaborator that understands your entire workflow, from drafting and research to coding and complex problem-solving. Start tackling bigger problems with Claude and unlock Claude Pro's full capabilities at https://claude.ai/tcr Serval: Serval uses AI-powered automations to cut IT help desk tickets by more than 50%, freeing your team from repetitive tasks like password resets and onboarding. Book your free pilot and guarantee 50% help desk automation by week 4 at https://serval.com/cognitive Tasklet: Tasklet is an AI agent that automates your work 24/7; just describe what you want in plain English and it gets the job done. Try it for free and use code COGREV for 50% off your first month at https://tasklet.ai CHAPTERS: (00:00) About the Episode (04:57) Homeschooling context and AI (15:55) Building an AI team (Part 1) (19:51) Sponsors: VCX | Claude (23:18) Building an AI team (Part 2) (31:03) Onboarding agents like employees (Part 1) (38:12) Sponsors: Serval | Tasklet (40:31) Onboarding agents like employees (Part 2) (40:57) Context, models, and privacy (48:47) AI intimacy and rights (56:19) Coordinating agents in Slack (01:02:19) Designing an agent superapp (01:08:35) Agent trust and kids (01:17:57) Voice interfaces for families (01:29:51) Curated screens and automations (01:40:28) Sharing setups and software (01:48:43) Local sovereignty and kid devices (01:59:26) Work, disruption, and play (02:04:58) Episode Outro (02:07:45) Outro PRODUCED BY: https://aipodcast.ing

In this episode, I am in conversation with Dr Christiane Tristl, an economic geographer interested in heterodox economic geography. Their scholarship focuses on big tech companies, digital technologies, marketisation of water and critical agri-food studies. We discuss her book Turning Water into Commodity: Digital Innovation and the Private Sector as Development Agent (Bristol UP, 2025). Dr Tristl's book explores how private sector approaches and digital technologies open up remote regions to permanent arrangements of transnational market-based water supply beyond state sovereignty, which define their users as paying customers. By considering the socio-political realities of these market based interventions in the water sector, Dr Tristl's research spells out for us the increasing influence of private corporations and philanthrocapitalist principles in development cooperation in both rural and peri-urban parts of Kenya.Abhilasha Jain is a social anthropologist trained at the London School of Economics. Her research interests lie at the intersection of caste, gender, spatial and climate justice, legal and critical anthropology. She is a qualitative researcher, curriculum designer and a feminist ethnographer. She has produced and co-hosted an academic podcast in India called AcademiaBTS, to bring graduates and PhD scholars to talk about their work, academic life in India, and to build a community that resonates with students in higher education.   Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/new-books-network

In this episode, I am in conversation with Dr Christiane Tristl, an economic geographer interested in heterodox economic geography. Their scholarship focuses on big tech companies, digital technologies, marketisation of water and critical agri-food studies. We discuss her book Turning Water into Commodity: Digital Innovation and the Private Sector as Development Agent (Bristol UP, 2025). Dr Tristl's book explores how private sector approaches and digital technologies open up remote regions to permanent arrangements of transnational market-based water supply beyond state sovereignty, which define their users as paying customers. By considering the socio-political realities of these market based interventions in the water sector, Dr Tristl's research spells out for us the increasing influence of private corporations and philanthrocapitalist principles in development cooperation in both rural and peri-urban parts of Kenya.Abhilasha Jain is a social anthropologist trained at the London School of Economics. Her research interests lie at the intersection of caste, gender, spatial and climate justice, legal and critical anthropology. She is a qualitative researcher, curriculum designer and a feminist ethnographer. She has produced and co-hosted an academic podcast in India called AcademiaBTS, to bring graduates and PhD scholars to talk about their work, academic life in India, and to build a community that resonates with students in higher education.   Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/world-affairs

In this episode, I am in conversation with Dr Christiane Tristl, an economic geographer interested in heterodox economic geography. Their scholarship focuses on big tech companies, digital technologies, marketisation of water and critical agri-food studies. We discuss her book Turning Water into Commodity: Digital Innovation and the Private Sector as Development Agent (Bristol UP, 2025). Dr Tristl's book explores how private sector approaches and digital technologies open up remote regions to permanent arrangements of transnational market-based water supply beyond state sovereignty, which define their users as paying customers. By considering the socio-political realities of these market based interventions in the water sector, Dr Tristl's research spells out for us the increasing influence of private corporations and philanthrocapitalist principles in development cooperation in both rural and peri-urban parts of Kenya.Abhilasha Jain is a social anthropologist trained at the London School of Economics. Her research interests lie at the intersection of caste, gender, spatial and climate justice, legal and critical anthropology. She is a qualitative researcher, curriculum designer and a feminist ethnographer. She has produced and co-hosted an academic podcast in India called AcademiaBTS, to bring graduates and PhD scholars to talk about their work, academic life in India, and to build a community that resonates with students in higher education.   Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/environmental-studies

In this episode, I am in conversation with Dr Christiane Tristl, an economic geographer interested in heterodox economic geography. Their scholarship focuses on big tech companies, digital technologies, marketisation of water and critical agri-food studies. We discuss her book Turning Water into Commodity: Digital Innovation and the Private Sector as Development Agent (Bristol UP, 2025). Dr Tristl's book explores how private sector approaches and digital technologies open up remote regions to permanent arrangements of transnational market-based water supply beyond state sovereignty, which define their users as paying customers. By considering the socio-political realities of these market based interventions in the water sector, Dr Tristl's research spells out for us the increasing influence of private corporations and philanthrocapitalist principles in development cooperation in both rural and peri-urban parts of Kenya.Abhilasha Jain is a social anthropologist trained at the London School of Economics. Her research interests lie at the intersection of caste, gender, spatial and climate justice, legal and critical anthropology. She is a qualitative researcher, curriculum designer and a feminist ethnographer. She has produced and co-hosted an academic podcast in India called AcademiaBTS, to bring graduates and PhD scholars to talk about their work, academic life in India, and to build a community that resonates with students in higher education.   Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/geography

In this episode, I am in conversation with Dr Christiane Tristl, an economic geographer interested in heterodox economic geography. Their scholarship focuses on big tech companies, digital technologies, marketisation of water and critical agri-food studies. We discuss her book Turning Water into Commodity: Digital Innovation and the Private Sector as Development Agent (Bristol UP, 2025). Dr Tristl's book explores how private sector approaches and digital technologies open up remote regions to permanent arrangements of transnational market-based water supply beyond state sovereignty, which define their users as paying customers. By considering the socio-political realities of these market based interventions in the water sector, Dr Tristl's research spells out for us the increasing influence of private corporations and philanthrocapitalist principles in development cooperation in both rural and peri-urban parts of Kenya.Abhilasha Jain is a social anthropologist trained at the London School of Economics. Her research interests lie at the intersection of caste, gender, spatial and climate justice, legal and critical anthropology. She is a qualitative researcher, curriculum designer and a feminist ethnographer. She has produced and co-hosted an academic podcast in India called AcademiaBTS, to bring graduates and PhD scholars to talk about their work, academic life in India, and to build a community that resonates with students in higher education.   Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/science-technology-and-society

In this episode, I am in conversation with Dr Christiane Tristl, an economic geographer interested in heterodox economic geography. Their scholarship focuses on big tech companies, digital technologies, marketisation of water and critical agri-food studies. We discuss her book Turning Water into Commodity: Digital Innovation and the Private Sector as Development Agent (Bristol UP, 2025). Dr Tristl's book explores how private sector approaches and digital technologies open up remote regions to permanent arrangements of transnational market-based water supply beyond state sovereignty, which define their users as paying customers. By considering the socio-political realities of these market based interventions in the water sector, Dr Tristl's research spells out for us the increasing influence of private corporations and philanthrocapitalist principles in development cooperation in both rural and peri-urban parts of Kenya.Abhilasha Jain is a social anthropologist trained at the London School of Economics. Her research interests lie at the intersection of caste, gender, spatial and climate justice, legal and critical anthropology. She is a qualitative researcher, curriculum designer and a feminist ethnographer. She has produced and co-hosted an academic podcast in India called AcademiaBTS, to bring graduates and PhD scholars to talk about their work, academic life in India, and to build a community that resonates with students in higher education.   Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/technology

OpenSea launches an agent CLI. Gitcoin proposes an overhaul of its grants program. And Curve accuses PancakeSwap of copying its code. Read more: https://ethdaily.io/898 Borrow against ETH at the lowest fixed rates in DeFi. Liquity V2 lets you use ETH as collateral to mint BOLD, the Ethereum native dollar. Learn more at liquity.org Content is for informational purposes only, not endorsement or investment advice. The accuracy of information is not guaranteed.

פרק מספר 512 (חזקה תשיעית!) של רברס עם פלטפורמה - קרבורטור מספר 40, שהוקלט ב-24 בפברואר 2026. נכון למועד ההקלטה עדיין אין מלחמה [לא התיישן טוב…], ואורי ורן מארחים את הנביא האורח נתי שלום לשיחה, דיונים, וויכוחים ותחזיות (דיסטופיות ברובן) על עולם שבו ה-AI כבר לא רק כותב קוד, אלא מחליף את המציאות כפי שהכרנו אותה. [01:58] "משהו גדול קורה": הניתוח של Matt Shumerבלוג-פוסט של המפתח Matt Shumer, שנקרא Something Big is Happening התפרסם בלא מעט מקומות והיכה גלים.מעבר מסקפטיות מוחלטת ("זה בחיים לא יעבוד") למצב שבו המודל עושה את כל עבודת הקידוד שלו.נתי - מה שמעניין פה זה הניתוח של שוק העבודה, ואיך נראה שוק ה-Hiring כפי שהוא היום.הדיבורים על "הכתובת על הקיר" זה כבר פאסה – "הכתובת היא כבר בכיס כמעט". הנתונים מראים ירידה משמעותית ב-Hiring שהתחילה כבר משנת 2025 ונמשכת לתוך 2026.“זה קורה עכשיו - ועכשיו אתה צריך לבחור באיזה צד אתה נמצא: הצד המרוויח או הצד הנפגע”.רן מדגיש שזה לא רק למפתחים – גם עורכי דין ורואי חשבון ובכל שאר המקצועות צריכים להחליט באיזה צד הם. יש כאן (לפחות) שני אספקטים עיקריים - איך אנחנו רואים את שוק התוכנה, ואז זה משפיע על כל שאר שוק העבודה.אורי - אנחנו רואים את ההשפעה מבפנים, בתוך שוק התוכנה. האם ישנן תעשיות שלא מושפעות עדיין, או לפחות לא מרגישות את זה?למשל יוצאי יחידות טכנולוגיות שמאוד מבוקשים בשוק, אבל ארגונים בטחוניים לא יכולים להכניס הרבה מהטכנולוגיות Cutting-edge הללו, לפחות לא בקצב שהן יוצאות.מועמדים כאלה אולי פתאום לא מתאימים בדיוק לעולם שרץ “בחוץ”.נתי משתף סיפור אישי/מקצועי על שיר אלגום, שנדחתה ממשרה ב-HR כי לא הכירה מספיק AI, ובתגובה הפכה למומחית שמרצה ב-Amazon.שינוי גישה: "העולם השתנה, הבנתי, אני עכשיו באירוע".אורי ונתי מחפשים השוואות למהפכות קודמות, ולא בטוחים אם יש כאלו בדיוק - מעבר משימוש ב-Intellect האנושי כדי לייצר יתרון - למצב בו "ה-Intellect עובר קומודיטיזציה".אין יותר Job security בהייטק המסורתי, וחזרה לכיוון של מקצועות יותר “מסורתיים”, פיזיים.[10:17] עידן ה-Agents וה-+Resumeנתי - קונספט של “Professional Agents”: מומחים כבר לא מוכרים את עצמם כעובדים, אלא כסוכנים, או ככאלה שמתמחים ביצירת סוכנים.סוכן הוא כמו ילד – צריך לגדל אותו ולשכלל אותו, דורש הרבה Nurturing.רן - ספציפית: מדברים על מעצבים, רואי-חשבון - מקצועות ספציפיים, שהם אולי לא חלק מהליבה של החברה, אבל נמצאים בכל חברה.נתי - דוגמא של Marketing: אם מישהו כבר הכין את רוב ה-Workflows מראש, זה משהו שאני מוכן לשלם עליו.אורי מציין שגם בגידול של ילד באיזשהו שלב עוברים ל-Outsourcing יותר ויותר . . . חברות עוברות לתת שירות של סוכן יחד עם “גידול סוכנים” ושכלול שלהם: סוכן + משהו שמתחזק אותו ומתאים אותו לצרכים שלך.הבשורה טובה: יש לאן להתפתח - בכל פעם שחסמי-כניסה יורדים, נפתחים תחומים חדשיםאורי ונתי קצת חלוקים על הנקודה, אבל זה דומה למה שהיה בתחילת ימי ה-SaaS, שאולי לא היה קיים אם לא היה Cloud, לפחות לא בקצב וב-Scale, שקודם לכן היה שמור לארגונים מאוד גדולים ולא לסטארטאפים.דוגמא דומה היא Big-Data.נתי אומר שהורדת חסמי-הכניסה תכניס הרבה גורמים חדשים לתחום, לאו דווקא רק מכיוון של מדעי-המחשב.אורי - השוני במהפכה הזו הוא שיש מצב שבו סוכן יכול לייצר סוכן יותר טוב . . . נתי מפריד בין מוצרים “גנריים” - יש את המודלים של Anthropic ו-OpenAI ומשפחות המוצרים הנגזרות וכו' - ובין ה”OpenClaw למיניהם”, שהם גרסא פשוטה יותר וזולה יותר, יחד עם קוד-פתוח ומוצרים בסגנון הזה.רן משווה את המאבק בין מודלים גנריים (כמו Anthropic) למודלים פתוחים (כמו OpenClaw) ל-"האנדרואיד לעומת האייפון".נתי מדבר על ראיון העבודה העתידי: “עובדים יבואו עם ה-10X של עצמם”: מועמדים לא יבואו עם קורות חיים, אלא עם רזומה פלוס – צוות סוכנים שבנו ושיודעים לשכלל להם את העבודה.בשנה-שנתיים-שלוש הקרובות, אלו שיעשו את הקפיצה ויבנו את הסוכנים וידעו להגיע עם זה לראיון עבודה - זו יכולה להיות הזדמנות לגדול ולהתבסס.אבל - אנחנו לא יודעים כמה ומי הולך להיפגע: “יהיה פה מצב של ירידה לטובת עלייה”.[17:03] “אז מה יכול לקרות?”: הסינגולריות והמתכנת האחרוןרן מעלה את השאלה המפחידה: האם כל הניסיון שצברנו כמפתחים הלך לפח? השנים הקרובות כנראה הולכות להיות מבלבלות, אבל ננסה להסתכל מעבר לזה.האם לא יהיו יותר מתכנתים, כי לא צריך - או שיהיו הרבה יותר מתכנתים והרבה יותר תוכנה, אבל מקצוע התכנות יראה אחרת?נתי חוזה ירידה למען עלייה - אבל בשונה מהמעבר ל-Cloud-Native למשל, שלקח בערך 10 שנים (ולא נגמר…), כאן הקצב הרבה יותר מהיר (התעשייה השתנתה בתוך שנה).זוכרים את “כולם משתמשים ב-AI, אבל לא רואים את ה-ROI”? זה היה בתחילת 2025 . . . מאז הסטטיסטיקות התחילו להשתנות.רן - “אם לפני שנה הייתי נותן ל-Agent משימות קידוד קטנות, ולפעמים זה מצליח ולפעעמים זה לא - היום זה עולם אחר לגמרי”.אז יכנסו יותר מעגלי-אוכלוסיה לתחום - אבל הצד השלילי הוא הירידה שלפני: כמות האנשים שדרושים למשימות שיש היום, עד שיווצר ה-Demand החדש, תגרום להרבה אנשים למצוא את עצמם “מחוץ למעגל”.מדינות תצטרכנה איכשהו לספוג את הירידה הזו - מימון הכשרות, תקופות הסתגלות וכו' - אחרת זו בדיוק הסביבה למהפכות והתדרדרות למקומות יותר בעייתיים.ולא שהסדר העולמי מסביב שליו ורגוע גם ככה [נתכתב מהממ”ד במהלך מלחמה באירן…].אורי - כבר רואים התחלה של “כלכלת סיליקון”, ומדינות nתחילות לחשוב על מאגרי הChip-ים שלהן . . . נתי מזכיר פרק של All-In, שמדבר על תחזיות מאוד אופטימיות, ועל פניו קצת מנותקות - “המון הזדמנויות והכל יהיה בסדר”, בזמן שמי שבתחום יודע שזה לא ממש ככה.נראה שב-Silicon Valley יש בעיקר התעלמות - חוגגים בתוך מעגל מאוד מצומצם.נתי מציע לחשוב על זה כמו על קורונה [במובן החיובי…] - נצטרך התערבות חיצונית כדי לעבור את הגל הזה.רן תוהה האם - בדומה לקורונה - גם התקופה הזו גם תיהיה קטליזטור לתאוריות קונספירציה שעוד תבואנה . . . אורי - מצד שני, גם תרבות הפנאי התפתחה מאוד בתקופת הקורונה, אולי שוב מישהו אחר עושה את העבודה ואז יש יותר פנאי?רן - כבר היום, כשאני מפתח, אני מספיק הרבה יותר, בהרבה פחות זמן. אז אנחנו מייצרים הרבה יותר תוכנה . . .אורי - אבל אז ה-bottlenecks עוברים למקומות אחרים.רן - OpenAI הזכירו, לגבי הפיתוח של Codex 5.3 – שהמודל פותח בעזרת גרסאות קודמות של עצמו."זה בערך By definition הסינגולריות" . . .“אל תצפו שהסינגולריות תקרה ביום אחד בודד” . . . “מי שהיה במהפכה התעשייתית לא יודע שהוא במהפכה התעשייתית".[27:57] חמשת ה-Moats של 2026נתי - האם נכון לבנות סטארטאפ באי ודאות כזו? מה הסיכוי של סטארטאפ כזה לשרוד?נאמר על רקע שבוע מאוד לא מוצלח למניות חברות ה-SaaS . . . .יש הרבה תגובות-יתר - אבל קורים הרבה דברים באמת מדהימים.נתי מציע 5 נקודות קריטיות ליזמים (סוג של Checklist) שרוצים לשרוד בעולם שבו כל דבר גנרי נמחק (כמו IBM שצנחה כי Anthropic פרסמו בלוג-פוסט על Cobol . . . ):ורטיקליזציה (Verticalization): אל תהיו גנריים. Google ו-Anthropic ו-OpenAI שולטים ביד רמה.תהיו הכי טובים במשהו ספציפי - עריכת דין או חינוך וכו'.שליטה במידע (Proprietary Data): דאטה שה-LLM הגדולים והמודלים הגנריים לא ראו, כמו מגמות ספציפיות בתוך נתוני לקוחות.יעילות (Efficiency): שימוש ב-SLM (Small Language Models) למשל, כדי לחסוך ב-Token-ים וב-Latency (קריטי ברובוטיקה וב-Security, למשל).רן - מודל גדול יקבל את ההחלטה הנכונה, אבל אולי מאוחר מדי.חווית משתתמש (UX ייחודי): חווית משתמש שפותרת בעיה נקודתית ונותנת ערך מהיר (Time to Value).ה-Chat של המודלים הגדולים מאוד גנרי.סטארטאפים צריכים להתמקד ביכולת לייצר חוויית משתמש מאוד מותאמת לחווייה נקודתית.רן - האם בכלל עוד יהיה UI (או שהצרכנים הם גם Agents . . . .)? בהקשר של פיקסלים . . . .נתי, אורי - בסוף , אתה רוצה לייצר ערך לאדם.בסוף זה עניין של Time to Value: אני אולי יכול לייצר את זה לבד, השאלה האם לא יותר מהיר ויעיל להשתמש במשהו שמישהו אחר כבר ייצר.ואחרון (אם כי נתי אמר ש "החמישי הוא לא לשידור…”) - Disruption: ה-Disruption האמיתי הוא לעשות קניבליזציה לקטגוריות ישנות.אפשר לעשות את אותם הדברים שעשינו בעבר, אבל בצורה אחרת לגמרי.הרבה דברים קודמים נעשו בגלל מגבלות של עולם שהוא Pre-Agentic, ועכשיו לא רלוונטיות - מה שמאפשר מודל עסקי אחר לחלוטין.ואז ה-Price-point יכול להיות מאוד שונה מכזה שהוכתב ע”י תעשיות מאוד גדולות ומבנה עלויות מאוד יקר לתפעול.אורי מתזכר את ה-Moats של Warren Buffet, ונתי מספר שהוא לא חושב שפגש חברה אחת שבאמת עושה את כל הדברים הללו, יזמים עדיין לא חושבים ככה.במיוחד בארץ, עדיין מתייחסים מאוד לבידול הטכנולוגי ופחות למובן של UX או מודל עסקי.[39:26] הזרקת DNA ומהלכי ה-M&A החדשיםנתי אומר שמשקיעים בהרבה מקרים לא יודעים לנתח הזדמנויות ולעשות Evaluation שלא על סמך טרנד צמיחה של ARR.אורי - עולם ההשקעות לא הולך לכיוון של SaaS, כי מצד אחד יש המון Disruption risk ומצד שני נראה שהצורך במגמת ירידה.נתי - יש כמה סוגי-Exists שונים שמשקיעים מחפשים, מעבר למודל הקלאסי של “תבנה חברה, תגדל איתה, תייצר מספיק כסף . . . .”.קנייה של טכנולוגיות ואנשים - חברות צריכות “להזריק לעצמן DNA חדש”, ואז מסתכלים על הסטראטאפ לא רק כטכנולוגיה אלא גם כמנוע לטרנספורציה.חברות במצוקה מנסות למצוא אנשים שיעזרו להן לעשות את הטרנספורמציה, לפחות בחלון הזמן הנוכחי (3 שנים בערך).נתי מזכיר דוגמא שעלתה בעבר - Google: לפני שנה כולם הספידו אותם, ואז הם קנו את Character.AI, ובעצם את נועם שזיר (Noam Shazeer) ב-2 ביליון דולר, כי הם הבינו שהם במצוקה.נתי טוען שלחברות במצוקה יהיה מאוד קשה לעשות כזה שינוי רק על ידי צמיחה אורגנית.אורי מדבר על חברות שעושות קניבליזציה-מוצרית לעצמן - מתחרים במוצר המסורתי הקודם שלהן.נתי טוען שבמקרה של Google זה השתלם להם עם Search Generative Experience (SGE).[46:00] סיכום וסגירהרן ממליץ לכולם לקרוא את הבלוג-פוסט של Matt Shumer (או לבקש מ-Agent לתקצר אותו).נתי חותם עם המלצה אופטימית-מעשית: "למדו את עצמכם... תחשבו שאתם באים למקום העבודה הבא שלכם כבר לא אתם-עצמכם... זה רזומה + צוות עובדים שאתם מביאים איתכם, שזה הסוכנים".אורי כבר מכין את הקרקע לפרק הבא: מהפכת ה-Quantum Computing."שיעורי הבית שלכם יכולים להיות 0, 1 או שניהם ביחד" . . . [קישור לקובץ mp3] האזנה נעימה ותודה רבה לעופר פורר על התמלול!

The smartphone is everywhere, but its next evolution won't look like the apps we use today. In this episode of Bold Names, WSJ's Tim Higgins sits down with Qualcomm CEO Cristiano Amon to discuss the seismic shift from apps to AI agents – and why this transition could reshape everything from your phone to your glasses. To watch the video version of this episode, visit our WSJ Podcasts YouTube channel or the video page of WSJ.com. Check Out Past Episodes: How SAP's CEO Is Remaking the European Tech Giant For The Age Of AI How Corning Is Using Trump's Tariffs To Its Advantage This Tech Founder's $1.3 Billion Company Is Taking On Apple and Samsung Biden's Antitrust Architect on How Big Tech Threatens U.S. Prosperity Let us know what you think of the show. Email us at BoldNames@wsj.com. Sign up for the WSJ's free Technology newsletter.Read Tim Higgins's column.  Learn more about your ad choices. Visit megaphone.fm/adchoices

During the 4pm hour of today's show Chuck & Chernoff talked about free agents the Braves can still target, the Falcons, Kyle Pitts and more before being joined by our Agent to the Stars, Hadley Engelhard. See omnystudio.com/listener for privacy information.

March 6, 2026: Your daily rundown of health and wellness news, in under 5 minutes. Today's top stories: Amazon Web Services launches Amazon Connect Health, AI-powered system automating healthcare admin work and reducing abandoned calls by 30% UBS projects global longevity spending will reach $8 trillion annually by 2030, with GLP-1 drugs alone surpassing $200B in sales this decade Oura acquires Finnish startup Doublepoint to bring biometric gesture controls to wearable ecosystem, supporting vision of "cloud of wearables" More from Fitt: Fitt Insider breaks down the convergence of fitness, wellness, and healthcare — and what it means for business, culture, and capital. Subscribe to our newsletter → insider.fitt.co/subscribe Work with our recruiting firm → https://talent.fitt.co/ Follow us on Instagram → https://www.instagram.com/fittinsider/ Follow us on LinkedIn → linkedin.com/company/fittinsider Reach out → insider@fitt.co

Has the cost of software development officially dropped below the minimum wage? Andrew and Ben examine this economic shift alongside the rapid open-source growth and security implications of the OpenClaw project. They also explore Steve Yegge's concept of a federated wasteland for orchestrators and how the new Perplexity Computer is stepping up to act as a persistent, always-on digital coworker.Follow the show:Subscribe to our Substack Follow us on LinkedInSubscribe to our YouTube ChannelLeave us a ReviewFollow the hosts:Follow AndrewFollow BenFollow DanFollow today's stories:OpenClaw rocks to GitHub's most-starred status, but is it safe?Welcome to the Wasteland: A Thousand Gas TownsIntroducing Perplexity ComputerSoftware development now costs less than than the wage of a minimum wage workerScott Werner's Works on My machineTraffic Jam ExplorerOFFERS Start Free Trial: Get started with LinearB's AI productivity platform for free. Book a Demo: Learn how you can ship faster, improve DevEx, and lead with confidence in the AI era. LEARN ABOUT LINEARB AI Code Reviews: Automate reviews to catch bugs, security risks, and performance issues before they hit production. AI & Productivity Insights: Go beyond DORA with AI-powered recommendations and dashboards to measure and improve performance. AI-Powered Workflow Automations: Use AI-generated PR descriptions, smart routing, and other automations to reduce developer toil. MCP Server: Interact with your engineering data using natural language to build custom reports and get answers on the fly.

Ryan Carson taught over 1,000,000 people how to code at Treehouse and spent 25% of his entire life doing it. Now he says everything about that process needs to change.In this livestream, Ryan joins Corey Noles and Grant Harvey to rethink programming education from scratch. When AI agents can write production code, pass competitive coding challenges, and ship features while you sleep.We'll cover:

What if the best way to solve problems… is to stop them from happening in the first place?In this episode of The Agent of Wealth Podcast, the Bautis Financial team discusses another book in their Book Club series: Upstream: The Quest to Solve Problems Before They Happen by Dan Heath.Through real-world examples and insights from the book, the team explores why organizations often get stuck reacting to problems instead of preventing them — and what it takes to shift toward upstream thinking.In this episode, we discuss:Why many problems persist due to the “diffusion of responsibility” — and how assigning clear ownership is critical to preventing recurring issues.How “tunneling” keeps individuals and organizations stuck in reactive mode, constantly solving urgent problems without addressing the root causes upstream.Why changing outcomes often requires redesigning systems — not just asking people within those systems to work harder or behave differently.How identifying leverage points, early warning signals and meaningful metrics can help organizations detect problems sooner and measure whether prevention efforts are truly working.And more!Tune in for a thoughtful discussion on how upstream thinking can lead to better decision-making, stronger systems and fewer crises — whether you're running a business, managing a team or simply trying to improve processes in your everyday life.Resources: Episode Transcript & Blog | Upstream: The Quest to Solve Problems Before They Happen | Bautis Financial: 8 Hillside Ave, Suite LL1 Montclair, New Jersey 07042 (862) 205-5000 | Schedule an Introductory CallWant to be a guest on The Agent of Wealth? Send Marc Bautis a message on PodMatch, here: https://tinyurl.com/mt4z6ywc

Animoca Brands Chairman Yat Siu makes a bold prediction: within the next few years, there will be 30 to 50 billion AI agents operating online. And they won't live on Facebook or Google — they'll live on the blockchain.In this wide-ranging conversation, Yat breaks down why crypto infrastructure was built for this moment, why Europe faces "technology colonization," and what Apple's 30% Patreon fee reveals about platform power.In this conversation:• Bitcoin acting as a safe haven vs. risk asset• Hong Kong's position as Asia's financial hub• The real state of NFTs and gaming• Animoca Combines: AI agents for everyone• His prediction: 30-50 billion agents on-chain• Why digital identity matters more for agents than humans• Agent swarm security experiments at AnimocaAbout the Guest:Yat Siu is the Chairman and Co-founder of Animoca Brands, one of the world's leading blockchain gaming and investment companies. Animoca Brands has made over 400 investments in Web3 projects.

How can AI Copywriting help Martin Booth, who has worked as a journalist and copywriter, but traditional clients are turning to large language models to draft their copy nowadays? An answer emerges during the conversation.Summary of PodcastThe rise of AI copywritingMartin shares his perspective on the growing impact of AI tools like ChatGPT on the copywriting industry. He explains how clients are increasingly turning to AI-generated content, which has reduced the demand for his services. The group discusses strategies for copywriters to adapt and remain relevant in this changing landscape.Opportunity in Answer Engine Optimisation (AEO)Graham introduces the concept of Answer Engine Optimisation (AEO) as a potential solution for copywriters to differentiate themselves. He explains AI copywriting is how AEO focuses on crafting content that is optimised for how AI language models like ChatGPT search and surface information, rather than traditional search engine optimisation (SEO) tactics. The group explores the technical and creative aspects of AEO, and the potential for Martin to position himself as an expert in this emerging field.Recap and next stepsKevin and Graham summarise the key insights from the discussion, emphasising the need for copywriters to adapt to the changing landscape and leverage AI tools in strategic ways. They express optimism about Martin's ability to capitalise on the AEO opportunity and become a thought leader in this space.The Next 100 Days Podcast Co-HostsGraham ArrowsmithGraham founded Finely Fettled eleven years ago to help businesses market to affluent and high-net-worth customers. He's the founder of MicroYES, a Partner of MeclabsAI, providing AI Agents, Workflows and Phone to Agent delivery systems. Now, Graham offers Answer Engine Optimisation so you get found by LLM search and Enterprise-level AI Solutions.Kevin ApplebyKevin specialises in finance transformation and implementing business change. He's the COO of GrowCFO, which provides both community and CPD-accredited training designed to grow the next generation of finance leaders. You can find Kevin on LinkedIn and at kevinappleby.comNOTE: Here's What Claude ThinksWhy copywriters are well-positioned:LLMs don't just scrape keywords; they evaluate coherence, clarity, and authoritative structureAnswer engines prioritise content that demonstrates expertise through well-reasoned arguments, not just SEO tricksCopywriters understand persuasion architecture - how to build credible cases that convince readers (and now, AI systems evaluating on behalf of readers)Direct response copywriters especially understand question-answer flow and anticipating objections - precisely what LLMs look for when synthesising answersThe critical shift required:Traditional copywriting optimises for human conversionAEO copywriting must optimise for AI comprehension then human conversionThis means explicit structure: clear topic sentences, logical progression, supporting evidence, contextual markersLess "clever" wordplay, more semantic clarity and entity relationshipsWhere copywriters have genuine advantage over AI-generated content:Domain expertise translation - taking complex client knowledge and structuring it authoritativelyEvidence marshaling - knowing which proofs, testimonials, data points establish credibilityQuestion anticipation - your direct response background means you already think in terms of buyer journey questionsUnderstanding what makes content citable and quotable by LLMsThe reality check: Those copywriters who were primarily executing formulaic landing pages or generic blog content were always vulnerable to AI displacement. But copywriters who can architect information authority - structuring expertise so AI engines recognise and cite it - that's a defensible, valuable skill.You're essentially asking: "Can craftspeople who've been building for human readers pivot to building for AI intermediaries who serve human readers?" The answer is yes, because the fundamental skill - persuasive information architecture - transfers directly.

Polygon launches an Agent CLI toolkit. Bitwise donates $100k to Protocol Guild. And Vitalik urges bolder Ethereum innovation. Read more: https://ethdaily.io/897 Earn 10% real yield on your dollars, fully onchain. Hold $BOLD, the only decentralized stablecoin rated A- by stablecoin agency, Bluechip. No vaults, no middlemen, no RWAs. Learn more on liquity.org/earn Content is for informational purposes only, not endorsement or investment advice. The accuracy of information is not guaranteed.

Argentina's CBI evaluation report is out. Four of six bidders were declared inadmissible.View the full article here.Subscribe to the IMI Daily newsletter here. 

During the 4pm hour of today's show Chuck & Chernoff talked about free agents the Braves can still target, the Falcons, Kyle Pitts and more before being joined by our Agent to the Stars, Hadley Engelhard. See omnystudio.com/listener for privacy information.

Today on the Salesforce Admins Podcast, we talk to Joshua Birk, Senior Director of Admin Evangelism at Salesforce. Join us as we chat about how to get started with Agent Script and how it helps admins build better AI agents. You should subscribe for the full episode, but here are a few takeaways from our […] The post How Does Agent Script Give Admins More Control? appeared first on Salesforce Admins.

It's another episiode of Insurance town from the Lazy river!! With Ben Morrow, Brenden Corr, Marques Ogden, Jessica, Casey Nelson, Caleb Cramer, etc. Its FunSponsorsCanopy Connect MAVGoli= Go.Goli.com/Mayor

Carl and Mike discuss a CBS article by agent Joel Corry where he talks about how much he'd ask for if he was negotiating for Kyle Pitts - 4yrs/70 million. The guys break down how interested or disinterested they'd be in making that deal happen.

The reception to our recent post on Code Reviews has been strong. Catch up!Amid a maelstrom of discussion on whether or not AI is killing SaaS, one of the top publicly listed SaaS companies in the world has just reported record revenues, clearing well over $1.1B in ARR for the first time with a 28% margin. As we comment on the pod, Aaron Levie is the rare public company CEO equally at home in both worlds of Silicon Valley and Wall Street/Main Street, by day helping 70% of the Fortune 500 with their Enterprise Advanced Suite, and yet by night is often found in the basements of early startups and tweeting viral insights about the future of agents.Now that both Cursor, Cloudflare, Perplexity, Anthropic and more have made Filesystems and Sandboxes and various forms of “Just Give the Agent a Box” cool (not just cool; it is now one of the single hottest areas in AI infrastructure growing 100% MoM), we find it a delightfully appropriate time to do the episode with the OG CEO who has been giving humans and computers Boxes since he was a college dropout pitching VCs at a Michael Arrington house party.Enjoy our special pod, with fan favorite returning guest/guest cohost Jeff Huber!Note: We didn't directly discuss the AI vs SaaS debate - Aaron has done many, many, many other podcasts on that, and you should read his definitive essay on it. Most commentators do not understand SaaS businesses because they have never scaled one themselves, and deeply reflected on what the true value proposition of SaaS is.We also discuss Your Company is a Filesystem:We also shoutout CTO Ben Kus' and the AI team, who talked about the technical architecture and will return for AIE WF 2026.Full Video EpisodeTimestamps* 00:00 Adapting Work for Agents* 01:29 Why Every Agent Needs a Box* 04:38 Agent Governance and Identity* 11:28 Why Coding Agents Took Off First* 21:42 Context Engineering and Search Limits* 31:29 Inside Agent Evals* 33:23 Industries and Datasets* 35:22 Building the Agent Team* 38:50 Read Write Agent Workflows* 41:54 Docs Graphs and Founder Mode* 55:38 Token FOMO Culture* 56:31 Production Function Secrets* 01:01:08 Film Roots to Box* 01:03:38 AI Future of Movies* 01:06:47 Media DevRel and EngineeringTranscriptAdapting Work for AgentsAaron Levie: Like you don't write code, you talk to an agent and it goes and does it for you, and you may be at best review it. That's even probably like, like largely not even what you're doing. What's happening is we are changing our work to make the agents effective. In that model, the agent didn't really adapt to how we work.We basically adapted to how the agent works. All of the economy has to go through that exact same evolution. Right now, it's a huge asset and an advantage for the teams that do it early and that are kinda wired into doing this ‘cause you'll see compounding returns. But that's just gonna take a while for most companies to actually go and get this deployed.swyx: Welcome to the Lane Space Pod. We're back in the chroma studio with uh, chroma, CEO, Jeff Hoover. Welcome returning guest now guest host.Aaron Levie: It's a pleasure. Wow. How'd you get upgraded to, uh, to that?swyx: Because he's like the perfect guy to be guest those for you.Aaron Levie: That makes sense actually, for We love context. We, we both really love context le we really do.We really do.swyx: Uh, and we're here with, uh, Aaron Levy. Welcome.Aaron Levie: Thank you. Good to, uh, good to be [00:01:00] here.swyx: Uh, yeah. So we've all met offline and like chatted a little bit, but like, it's always nice to get these things in person and conversation. Yeah. You just started off with so much energy. You're, you're super excited about agents.I loveAaron Levie: agents.swyx: Yeah. Open claw. Just got by, got bought by OpenAI. No, not bought, but you know, you know what I mean?Aaron Levie: Some, some, you know, acquihire. Executiveswyx: hire.Aaron Levie: Executive hire. Okay. Executive hire. Say,swyx: hey, that's my term. Okay. Um, what are you pounding the table on on agents? You have so many insightful tweets.Why Every Agent Needs a BoxAaron Levie: Well, the thing that, that we get super excited by that I think is probably, you know, should be relatively obvious is we've, we've built a platform to help enterprises manage their files and their, their corporate files and the permissions of who has access to those files and the sharing collaboration of those files.All of those files contain really, really important information for the enterprise. It might have your contracts, it might have your research materials, it might have marketing information, it might have your memos. All that data obviously has, you know, predominantly been used by humans. [00:02:00] But there's been one really interesting problem, which is that, you know, humans only really work with their files during an active engagement with them, and they kind of go away and you don't really see them for a long time.And all of a sudden, uh, with the power of AI and AI agents, all of that data becomes extremely relevant as this ongoing source of, of answers to new questions of data that will transform into, into something else that, that produces value in your organization. It, it contains the answer to the new employee that's onboarding, that needs to ramp up on a project.Um, it contains the answer to the right thing to sell a customer when you're having a conversation to them, with them contains the roadmap information that's gonna produce the next feature. So all that data. That previously we've been just sort of storing and, and you know, occasionally forgetting about, ‘cause we're only working on the new active stuff.All of that information becomes valuable to the enterprise and it's gonna become extremely valuable to end users because now they can have agents go find what they're looking for and produce new, new [00:03:00] value and new data on that information. And it's gonna become incredibly valuable to agents because agents can roam around and do a bunch of work and they're gonna need access to that data as well.And um, and you know, sometimes that will be an agent that is sort of working on behalf of, of, of you and, and effectively as you as and, and they are kind of accessing all of the same information that you have access to and, and operating as you in the system. And then sometimes there's gonna be agents that are just.Effectively autonomous and kind of run on their own and, and you're gonna collaborate and work with them kind of like you did another person. Open Claw being the most recent and maybe first real sort of, you know, kind of, you know, up updating everybody's, you know, views of this landscape version of, of what that could look like, which is, okay, I have an agent.It's on its own system, it's on its own computer, it has access to its own tools. I probably don't give it access to my entire life. I probably communicate with it like I would an assistant or a colleague and then it, it sort of has this sandbox environment. So all of that has massive implications for a platform that manage that [00:04:00] enterprise data.We think it's gonna just transform how we work with all of the enterprise content that we work with, and we just have to make sure we're building the right platform to support that.swyx: The sort of shorthand I put it is as people build agents, everybody's just realizing that every agent needs a box. Yes.And it's nice to be called box and just give everyone a box.Aaron Levie: Hey, I if I, you know, if we can make that go viral, uh, like I, I think that that terminology, I, that's theswyx: tagline. Every agentAaron Levie: needs a box. Every agent needs a box. If we can make that the headline of this, I'm fine with this. And that's the billboard I wanna like Yeah, exactly.Every agent needs a box. Um, I like it. Can we ship this? Like,swyx: okay, let's do it. Yeah.Aaron Levie: Uh, my work here is done and I got the value I needed outta this podcast Drinks.swyx: Yeah.Agent Governance and IdentityAaron Levie: But, but, um, but, but, you know, so the thing that we, we kind of think about is, um, is, you know, whether you think the number 10 x or a hundred x or whatever the number is, we're gonna have some order of magnitude more agents than people.That's inevitable. It has to happen. So then the question is, what is the infrastructure that's needed to make all those agents effective in the enterprise? Make sure that they are well governed. Make sure they're only doing [00:05:00] safe things on your information. Make sure that they're not getting exposed. The data that they shouldn't have access to.There's gonna be just incredibly spectacularly crazy security incidents that will happen with agents because you'll prompt, inject an agent and sort of find your way through the CRM system and pull out data that you shouldn't have access to. Oh, weJeff Huber: have God,Aaron Levie: right? I mean, that's just gonna happen all over the place, right?So, so then the thing is, is how do you make sure you have the right security, the permissions, the access controls, the data governance. Um, we actually don't yet exactly know in many cases how we're gonna regulate some of these agents, right? If you think about an agent in financial services, does it have the exact same financial sort of, uh, requirements that a human did?Or is it, is the risk fully on the human that was interacting or created the agent? All open questions, but no matter what, there's gonna need to be a layer that manages the, the data they have access to, the workflows that they're involved in, pulling up data from multiple systems. This is the new infrastructure opportunity in the era of agents.swyx: You have a piece on agent identities, [00:06:00] which I think was today, um, which I think a lot of breaking news, the security, security people are talking about, right? Like you basically, I, I always think of this as like, well you need the human you and then there you need the agent. YouAaron Levie: Yes.swyx: And uh, well, I don't know if it's that simple, but is box going to have an opinion on that or you're just gonna be like, well we're just the sort of the, the source layer.Yeah. Let's Okta of zero handle that.Aaron Levie: I think we're gonna have an opinion and we will work with generally wherever the contours of the market end up. Um, and the reason that we're gonna have an opinion more than other topics probably is because one of the biggest use cases for why your agent might need it, an identity is for file system access.So thus we have to kind of think about this pretty deeply. And I think, uh, unless you're like in our world thinking about this particular problem all day long, it might be, you know, like, why is this such a big deal? And the reason why it's a really big deal is because sometimes sort of say, well just give the agent an, an account on the system and it just treats, treat it like every other type of user on the system.The [00:07:00] problem is, is that I as Aaron don't really have any responsibility over anybody else's box account in our organization. I can't see the box account of any other employee that I work with. I am not liable for anything that they do. And they have, I have, I have, you know, strict privacy requirements on everything that they're able to, you know, that, that, that they work on.Agents don't have that, you know, don't have those properties. The person who creates the agent probably is gonna, for the foreseeable future, take on a lot of the liability of what that agent does. That agent doesn't deserve any privacy because, because it's, you know, it can't fully be autonomously operated and it doesn't have any legal, you know, kind of, you know, responsibility.So thus you can't just be like, oh, well I'll just create a bunch of accounts and then I'll, I'll kind of work with that agent and I'll talk to it occasionally. Like you need oversight of that. And so then the question is, how do you have a world where the agent, sometimes you have oversight of, but what if that agent goes and works with other people?That person over there is collaborating with the agent on something you shouldn't have [00:08:00] access to what they're doing. So we have all of these new boundaries that we're gonna have to figure out of, of, you know, it's really, really easy. So far we've been in, in easy mode. We've hit the easy button with ai, which is the agent just is you.And when you're in quad code and you're in cursor, and you're in Codex, you're just, the agent is you. You're offing into your services. It can do everything you can do. That's the easy mode. The hard mode is agents are kind of running on their own. People check in with them occasionally, they're doing things autonomously.How do you give them access to resources in the enterprise and not dramatically increased the security risk and the risk that you might expose the wrong thing to somebody. These are all the new problems that we have to get solved. I like the identity layer and, and identity vendors as being a solution to that, but we'll, we'll need some opinions as well because so many of the use cases are these collaborative file system use cases, which is how do I give it an agent, a subset of my data?Give it its own workspace as well. ‘cause it's gonna need to store off its own information that would be relevant for it. And how do I have the right oversight into that? [00:09:00]Jeff Huber: One thing, which, um, I think is kind interesting, think about is that you know, how humans work, right? Like I may not also just like give you access to the whole file.I might like sit next to you and like scroll to this like one part of the file and just show you that like one part and like, you know,swyx: partial file access.Jeff Huber: I'm just saying I think like our, like RA does seem to be dead, right? Like you wanna say something is dead uhhuh probably RA is dead. And uh, like the auth story to me seems like incredibly unsolved and unaddressed by like the existing state of like AI vendors.ButAaron Levie: yeah, I think, um, we're, I mean you're taking obviously really to level limit that we probably need to solve for. Yeah. And we built an access control system that was, was kind of like, you know, its own little world for, for a long time. And um, and the idea was this, it's a many to many collaboration system where I can give you any part of the file system.And it's a waterfall model. So if I give you higher up in the, in the, in the system, you get everything below. And that, that kind of created immense flexibility because I can kind of point you to any layer in the, in the tree, but then you're gonna get access to everything kind of below it. And that [00:10:00] mostly is, is working in this, in this world.But you do have to manage this issue, which is how do I create an agent that has access to some of my stuff and somebody else's stuff as well. Mm-hmm. And which parts do I get to look at as the creator of the agent? And, and these are just brand new problems? Yeah. Crazy. And humans, when there was a human there that was really easy to do.Like, like if the three of us were all sharing, there'd be a Venn diagram where we'd have an overlapping set of things we've shared, but then we'd have our own ways that we shared with each other. In an agent world, somebody needs to take responsibility for what that agent has access to and what they're working on.These are like the, some of the most probably, you know, boring problems for 98% of people on, on the internet, but they will be the problems that are the difference between can you actually have autonomous agents in an enterprise contextswyx: Yeah.Aaron Levie: That are not leaking your data constantly.swyx: No. Like, I mean, you know, I run a very, very small company for my conference and like we already have data sensitivity issues.Yes. And some of my team members cannot see Yes. Uh, the others and like, I can't imagine what it's like to run a Fortune 500 and like, you have to [00:11:00] worry about this. I'm just kinda curious, like you, you talked to a lot like, like 70, 80% of your cus uh, of the Fortune 500, your customers.Aaron Levie: Yep. 67%. Just so we're being verySEswyx: precise.So Yeah. I'm notAaron Levie: Okay. Okay.swyx: Something I'm rounding up. Yes. Round up. I'm projecting to, forAaron Levie: the government.swyx: I'm projecting to the end of the year.Aaron Levie: Okay.swyx: There you go.Aaron Levie: You do make it sound like, like we, we, well we've gotta be on this. Like we're, we're taking way too long to get to 80%. Well,swyx: no, I mean, so like. How are they approaching it?Right? Because you're, you don't have a, you don't have a final answer yet.Why Coding Agents Took Off FirstAaron Levie: Well, okay, so, so this is actually, this is the stark reality that like, unfortunately is the kinda like pouring the water on the party a little bit.swyx: Yes.Aaron Levie: We all in Silicon Valley are like, have the absolute best conditions possible for AI ever.And I think we all saw the dke, you know, kind of Dario podcast and this idea of AI coding. Why is that taken off? And, and we're not yet fully seeing it everywhere else. Well, look, if you just like enumerated the list of properties that AI coding has and then compared it to other [00:12:00] knowledge work, let's just, let's just go through a few of them.Generally speaking, you bring on a new engineer, they have access to a large swath of the code base. Like, there's like very, like you, just, like new engineer comes on, they can just go and find the, the, the stuff that they, they need to work with. It's a fully text in text out. Medium. It's only, it's just gonna be text at the end of the day.So it's like really great from a, from just a, uh, you know, kinda what the agent can work with. Obviously the models are super trained on that dataset. The labs themselves have a really strong, kind of self-reinforcing positive flywheel of why they need to do, you know, agent coding deeply. So then you get just better tooling, better services.The actual developers of the AI are daily users of the, of the thing that they're we're working on versus like the, you know, probably there's only like seven Claude Cowork legal plugin users at Anthropic any given day, but there's like a couple thousand Claude code and you know, users every single day.So just like, think about which one are they getting more feedback on. All day long. So you just go through this list. You have a, you know, everybody who's a [00:13:00] developer by definition is technical so they can go install the latest thing. We're all generally online, or at least, you know, kinda the weird ones are, and we're all talking to each other, sharing best practices, like that's like already eight differences.Versus the rest of the economy. Every other part of the economy has like, like six to seven headwinds relative to that list. You go into a company, you're a banker in financial services, you have access to like a, a tiny little subset of the total data that's gonna be relevant to do your job. And you're have to start to go and talk to a bunch of people to get the right data to do your job because Sally didn't add you to that deal room, you know, folder.And that that, you know, the information is actually in a completely different organization that you now have to go in and, and sort of run into. And it's like you have this endless list of access controls and security. As, as you talked about, you have a medium, which is not, it's not just text, right? You have, you have a zoom call that, that you're getting all of the requirements from the customer.You have a lot of in-person conversations and you're doing in-person sales and like how do you ever [00:14:00] digitize all of that information? Um, you know, I think a lot of people got upset with this idea that the code base has all the context, um, that I don't know if you follow, you know, did you follow some of that conversation that that went viral?Is like, you know, it's not that simple that, that the code base doesn't have all the knowledge, but like it's a lot, you're a lot better off than you are with other areas of knowledge work. Like you, we like, we like have documentation practices, you write specifications. Those things don't exist for like 80% of work that happens in the enterprise.That's the divide that we have, which is, which is AI coding has, has just fully, you know, where we've reached escape velocity of how powerful this stuff is, and then we're gonna have to find a way to bring that same energy and momentum, but to all these other areas of knowledge work. Where the tools aren't there, the data's not set up to be there.The access controls don't make it that easy. The context engineering is an incredibly hard problem because again, you have access control challenges, you have different data formats. You have end users that are gonna need to kind of be kind of trained through this as opposed to their adopting [00:15:00] these tools in their free time.That's where the Fortune 500 is. And so we, I think, you know, have to be prepared as an industry where we are gonna be on a multi-year march to, to be able to bring agents to the enterprise for these workflows. And I think probably the, the thing that we've learned most in coding that, that the rest of the world is not yet, I think ready for, I mean, we're, they'll, they'll have to be ready for it because it's just gonna inevitably happen is I think in coding.What, what's interesting is if you think about the practice of coding today versus two years ago. It's probably the most changed workflow in maybe the history of time from the amount of time it's changed, right? Yeah. Like, like has any, has any workflow in the entire economy changed that quickly in terms of the amount of change?I just, you know, at least in any knowledge worker workflow, there's like very rarely been an event where one piece of technology and work practice has so fundamentally, you know, changed, changed what you do. Like you don't write code, you talk to an agent and it goes and [00:16:00] does it for you, and you may be at best review it.And even that's even probably like, like largely not even what you're doing. What's happening is we are changing our work to make the agents effective. In that model, the agent didn't really adapt to how we work. We basically adapted to how the agent works. Mm-hmm. All of the economy has to go through that exact same evolution.The rest of the economy is gonna have to update its workflows to make agents effective. And to give agents the context that they need and to actually figure out what kind of prompting works and to figure out how do you ensure that the agent has the right access to information to be able to execute on its work.I, you know, this is not the panacea that people were hoping for, of the agent drops in, just automates your life. Like you have to basically re-engineer your workflow to get the most out of agents and, uh, and that, that's just gonna take, you know, multiple years across the economy. Right now it's a huge asset and an advantage for the teams that do it early and that are kinda wired into doing this.‘cause [00:17:00] you'll see compounding returns, but that's just gonna take a while for most companies to actually go and get this deployed.swyx: I love, I love pushing back. I think that. That is what a lot of technology consultants love to hear this sort of thing, right? Yeah, yeah, yeah. First to, to embrace the ai. Yes. To get to the promised land, you must pay me so much money to a hundred percent to adopt the prescribed way of, uh, conforming to the agents.Yes. And I worry that you will be eclipsed by someone else who says, no, come as you are.Aaron Levie: Yeah.swyx: And we'll meet you where you are.Aaron Levie: And, and, and and what was the thing that went viral a week ago? OpenAI probably, uh, is hiring F Dees. Yeah. Uh, to go into the enterprise. Yeah. Yeah. And then philanthropic is embedded at Goldman Sachs.Yeah. So if the labs are having to do this, if, if the labs have decided that they need to hire FDE and professional services, then I think that's a pretty clear indication that this, there's no easy mode of workflow transformation. Yeah. Yeah. So, so to your point, I think actually this is a market opportunity for, you know, new professional services and consulting [00:18:00] firms that are like Agent Build and they, and they kind of, you know, go into organizations and they figure out how to re-engineer your workflows to make them more agent ready and get your data into the right format and, you know, reconstruct your business process.So you're, you're not doing most of the work. You're telling agents how to do the work and then you're reviewing it. But I haven't seen the thing that can just drop in and, and kinda let you not go through those changes.swyx: I don't know how that kind of sales pitch goes over. Yeah. You know, you're, you're saying things like, well, in my sort of nice beautiful walled garden, here's, there's, uh, because here's this, here's this beautiful box account that has everything.Yes. And I'm like, well, most, most real life is extremely messy. Sure. And like, poorly named and there duplicate this outdated s**tAaron Levie: a hundred percent. And so No, no, a hundred percent. And so this is actually No. So, so this is, I mean, we agree that, that getting to the beautiful garden is gonna be tough.swyx: Yeah.Aaron Levie: There's also the other end of the spectrum where I, I just like, it's a technical impossibility to solve. The agent is, is truly cannot get enough context to make the right decision in, in the, in the incredibly messy land. Like there's [00:19:00] no a GI that will solve that. So, so we're gonna have to kind of land in somewhere in between, which is like we all collectively get better at.Documentation practices and, and having authoritative relatively up-to-date information and putting it in the right place like agents will, will certainly cause us to be much better organized around how we work with our information, simply because the severity of the agent pulling the wrong data will be too high and the productivity gain of that you'll miss out on by not doing this will be too high as well, that you, that your competition will just do it and they'll just have higher velocity.So, uh, and, and we, we see this a lot firsthand. So we, we build a series of agents internally that they can kind of have access to your full box account and go off and you give it a task and it can go find whatever information you're looking for and work with. And, you know, thank God for the model progress, but like, if, if you gave that task to an agent.Nine months ago, you're just gonna get lots of bogus answers because it's gonna, it's gonna say, Hey, here's, here are fi [00:20:00] five, you know, documents that all kind of smell like the right thing. And I'm gonna, but I, but you're, you're putting me on the clock. ‘cause my assistant prompt says like, you know, be pretty smart, but also try and respond to the user and it's gonna respond.And it's like, ah, it got the wrong document. And then you do that once or twice as a knowledge worker and you're just neverswyx: again,Aaron Levie: never again. You're just like done with the system.swyx: Yeah. It doesn't work.Aaron Levie: It doesn't work. And so, you know, Opus four six and Gemini three one Pro and you know, whatever the latest five 3G BT will be, like, those things are getting better and better and it's using better judgment.And this sort of like the, all of these updates to the agentic tool and search systems are, are, we're seeing, we're seeing very real progress where the agent. Kind of can, can almost smell some things a little bit fishy when it's getting, you know, we, we have this process where we, we have it go fan out, do a bunch of searches, pull up a bunch of data, and then it has to sort of do its own ranking of, you know, what are the right documents that, that it should be working with.And again, like, you know, the intelligence level of a model six months ago, [00:21:00] it'd be just throwing a dart at like, I'm just, I'm gonna grab these seven files and I, I pray, I hope that that's the right answer. And something like an opus first four five, and now four six is like, oh, it's like, no, that one doesn't seem right relative to this question because I'm seeing some signal that is making that, you know, that's contradicting the document where it would normally be in the tree and who should have access.Like it's doing all of that kind of work for you. But like, it still doesn't work if you just have a total wasteland of data. Like, it's just not, it's just not possible. Partly ‘cause a human wouldn't even be able to do it. So basically if a, if a really, really smart human. Could not do that task in five or 10 minutes for a search retrieval type task.Look, you know, your agent's not gonna be able to do it any better. You see this all day long. SoContext Engineering and Search Limitsswyx: this touches on a thing that just passionate about it was just context engineering. I, I'm just gonna let you ramble or riff on, on context engineering. If, if, if there's anything like he, he did really good work on context fraud, which has really taken over as like the term that people use and the referenceAaron Levie: a hundred percent.We, we all we think about is, is the context rob problem. [00:22:00]Jeff Huber: Yeah, there's certainly a lot of like ranking considerations. Gentech surgery think is incredibly promising. Um, yeah, I was trying to generate a question though. I think I have a question right now. Swyx.Aaron Levie: Yeah, no, but like, like I think there was this moment, um, you know, like, I don't know, two years ago before, before we knew like where the, the gotchas were gonna be in ai and I think someone was like, was like, well, infinite context windows will just solve all of these problems and ‘cause you'll just, you'll just give the context window like all the data and.It's just like, okay, I mean, maybe in 2035, like this is a viable solution. First of all, it, it would just, it would just simply cost too much. Like we just can't give the model like the 5,000 documents that might be relevant and it's gonna read them all. And I've seen enough to, to start believing in crazy stuff.So like, I'm willing to just say, sure. Like in, in 10 years from now,swyx: never say, never, never.Aaron Levie: In, in 10 years from now, we'll have infinite context windows at, at a thousandth of the price of today. Like, let's just like believe that that's possible, but Right. We're in reality today. So today we have a context engineering [00:23:00] problem, which is, I got, I got, you know, 200,000 tokens that I can work with, or prob, I don't even know what the latest graph is before, like massive degradation.16. Okay. I have 60,000 tokens that I get to work with where I'm gonna get accurate information. That's not a lot of tokens for a corpus of 10 million documents that a knowledge worker might have across all of the teams and all the projects and all the people they work with. I have, I have 10 million documents.Which, you know, maybe is times five pages per document or something like that. I'm at 50 million pages of information and I have 60,000 tokens. Like, holy s**t. Yeah. This is like, how do I bridge the 50 million pages of information with, you know, the couple hundred that I get to work with in that, in that token window.Yeah. This is like, this is like such an interesting problem and that's why actually so much work is actually like, just like search systems and the databases and that layer has to just get so locked in, but models getting better and importantly [00:24:00] knowing when they've done a search, they found the wrong thing, they go back, they check their work, they, they find a way to balance sort of appeasing the user versus double checking.We have this one, we have this one test case where we ask the agent to go find. 10 pieces of information.swyx: Is this the complex work eval?Aaron Levie: Uh, this is actually not in the eval. This is, this is sort of just like we have a bunch of different, we have a bunch of internal benchmark kind of scenarios. Every time we, we update our agent, we have one, which is, I ask it to find all of our office addresses, and I give it the list of 10 offices that we have.And there's not one document that has this, maybe there should be, that would be a great example of the kind of thing that like maybe over time companies start to, you know, have these sort of like, what are the canonical, you know, kind of key areas of knowledge that we need to have. We don't seem to have this one document that says, here are all of our offices.We have a bunch of documents that have like, here's the New York office and whatever. So you task this agent and you, you get, you say, I need the addresses for these 10 offices. Okay. And by the way, if you do this on any, you know, [00:25:00] public chat model, the same outcome is gonna happen. But for a different kind of query, you give it, you say, I need these 10 addresses.How many times should the agent go and do its search before it decides whether or not, there's just no answer to this question. Often, and especially the, the, let's say lower tier models, it'll come back and it'll give you six of the 10 addresses. And it'll, and I'll just say I couldn't find the otherswyx: four.It, it doesn't know what It doesn't know. ItAaron Levie: doesn't know what It doesn't know. Yeah. So the model is just like, like when should it stop? When should it stop doing? Like should it, should it do that task for literally an hour and just keep cranking through? Maybe I actually made up an office location and it doesn't know that I made it up and I didn't even know that I made it up.Like, should it just keep, re should it read every single file in your entire box account until it, until it should exhaust every single piece of information.swyx: Expensive.Aaron Levie: These are the new problems that we have. So, you know, something like, let's say a new opus model is sort of like, okay, I'm gonna try these types of queries.I didn't get exactly what I wanted. I'm gonna try again. I'm gonna, at [00:26:00] some point I'm gonna stop searching. ‘cause I've determined that that no amount of searching is gonna solve this problem. I'm just not able to do it. And that judgment is like a really new thing that the model needs to be able to have.It's like, when should it give up on a task? ‘cause, ‘cause you just don't, it's a can't find the thing. That's the real world of knowledge, work problems. And this is the stuff that the coding agents don't have to deal with. Because they, it just doesn't like, like you're not usually asking it about, you're, you're always creating net new information coming right outta the model for the most part.Obviously it has to know about your code base and your specs and your documentation, but, but when you deploy an agent on all of your data that now you have all of these new problems that you're dealing withJeff Huber: our, uh, follow follow-up research to context ride is actually on a genetic search. Ah. Um, and we've like right, sort of stress tested like frontier models and their ability to search.Um, and they're not actually that good at searching. Right. Uh, so you're sort of highlighting this like explore, exploit.swyx: You're just say, Debbie, Donna say everything doesn't work. Like,Aaron Levie: well,Jeff Huber: somebody has to be,Aaron Levie: um, can I just throw out one more thing? Yeah. That is different from coding and, and the rest [00:27:00] of the knowledge work that I, I failed to mention.So one other kind of key point is, is that, you know, at the end of the day. Whether you believe we're in a slop apocalypse or, or whatever. At the end of the day, if you, if you build a working product at the end of, if you, if you've built a working solution that is ultimately what the customer is paying for, like whether I have a lot of slop, a little slop or whatever, I'm sure there's lots of code bases we could go into in enterprise software companies where it's like just crazy slop that humans did over a 20 year period, but the end customer just gets this little interface.They can, they can type into it, it does its thing. Knowledge work, uh, doesn't have that property. If I have an AI model, go generate a contract and I generate a contract 20 times and, you know, all 20 times it's just 3% different and like that I, that, that kind of lop introduces all new kinds of risk for my organization that the code version of that LOP didn't, didn't introduce.These are, and so like, so how do you constrain these models to just the part that you want [00:28:00] them to work on and just do the thing that you want them to do? And, and, you know, in engineering, we don't, you can't be disbarred as an engineer, but you could be disbarred as a lawyer. Like you can do the wrong medical thing In healthcare, you, there's no, there's no equivalent to that of engineering.Like, doswyx: you want there to be, because I've considered softwareJeff Huber: engineer. What's that? Civil engineering there is, right? NotAaron Levie: software civil engineer. Sure. Oh yeah, for sure. But like in any of our companies, you like, you know, you'll be forgiven if you took down the site and, and we, we will do a rollback and you'll, you'll be in a meeting, but you have not been disbarred as an engineer.We don't, we don't change your, you know, your computer science, uh, blameJeff Huber: degree, this postmortem.Aaron Levie: Yeah, exactly. Exactly. So, so, uh, now maybe we collectively as an industry need to figure out like, what are you liable for? Not legally, but like in a, in a management sense, uh, of these agents. All sorts of interesting problems that, that, that, uh, that have to come out.But in knowledge work, that's the real hostile environments that we're operating in. Hmm.swyx: I do think like, uh, a lot of the last year's, 2025 story was the rise of coding agents and I think [00:29:00] 2026 story is definitely knowledge work agents. Yes. A hundredAaron Levie: percent.swyx: Right. Like that would, and I think open claw core work are just the beginning.Yes. Like it's, the next one's gonna just gonna be absolute craziness.Aaron Levie: It it is. And, and, uh, and it's gonna be, I mean, again, like this is gonna be this, this wave where we, we are gonna try and bring as many of the practices from coding because that, that will clearly be the forefront, which is tell an agent to go do something and has an access to a set of resources.You need to be responsible for reviewing it at the end of the process. That to me is the, is the kind of template that I just think goes across knowledge, work and odd. Cowork is a great example. Open Closet's a great example. You can kind of, sort of see what Codex could become over time. These are some, some really interesting kind of platforms that are emerging.swyx: Okay. Um, I wanted to, we touched on evals a little bit. You had, you had the report that you're gonna go bring up and then I was gonna go into like, uh, boxes, evals, but uh, go ahead. Talk about your genetic search thing.Jeff Huber: Yeah. Mostly I think kinda a few of the insights. It's like number one frontier model is not good at search.Humans have this [00:30:00] natural explore, exploit trade off where we kinda understand like when to stop doing something. Also, humans are pretty good at like forgetting actually, and like pruning their own context, whereas agents are not, and actually an agent in their kind of context history, if they knew something was bad and they even, you could see in the trace the reason you trace, Hey, that probably wasn't a good idea.If it's still in the trace, still in the context, they'll still do it again. Uhhuh. Uh, and so like, I think pruning is also gonna be like, really, it's already becoming a thing, right? But like, letting self prune the con windowsswyx: be a big deal. Yeah. So, so don't leave the mistake. Don't leave the mistake in there.Cut out the mistake but tell it that you made a mistake in the past and so it doesn't repeat it.Jeff Huber: Yeah. But like cut it out so it doesn't get like distracted by it again. ‘cause really, you know, what is so, so it will repeat its mistake just because it's been, it's inswyx: theJeff Huber: context. It'sAaron Levie: in the context so much.That's a few shot example. Even if it, yeah.Jeff Huber: It's like oh thisAaron Levie: is a great thing to go try even ifJeff Huber: it didn't work.Aaron Levie: Yeah,Jeff Huber: exactly.Aaron Levie: SoJeff Huber: there's like a bunch of stuff there. JustAaron Levie: Groundhogs Day inside these models. Yeah. I'm gonna go keep doing the same wrongJeff Huber: thing. Covering sense. I feel like, you know, some creator analogy you're trying like fit a manifold in latent space, which kind is doing break program synthesis, which is kinda one we think about we're doing right.Like, you know, certain [00:31:00] facts might be like sort of overly pitting it. There are certain, you know, sec sectors of latent space and so like plug clean space. Yeah. And, uh, andswyx: so we have a bell, our editor as a bell every time you say that. SoJeff Huber: you have, you have to like remove those, likeswyx: you shoulda a gong like TPN or something.IfJeff Huber: we gong, you either remove those links to like kinda give it the freedom, kind of do what you need to do. So, but yeah. We'll, we'll release more soon. That'sAaron Levie: awesome.Jeff Huber: That'll, that'll be cool.swyx: We're a cerebral podcast that people listen to us and, and sort of think really deep. So yeah, we try to keep it subtle.Okay. We try to keep it.Aaron Levie: Okay, fine.Inside Agent Evalsswyx: Um, you, you guys do, you guys do have EVs, you talked about your, your office thing, but, uh, you've been also promoting APEX agents and complex work. Uh, yeah, whatever you, wherever you wanna take this just Yeah. How youAaron Levie: Apex is, is obviously me, core's, uh, uh, kind of, um, agent eval.We, we supported that by sort of. Opening up some data for them around how we kind of see these, um, data workspaces in, in the, you know, kind of regular economy. So how do lawyers have a workspace? How do investment bankers have a workspace? What kind of data goes into those? And so we, [00:32:00] we partner with them on their, their apex eval.Our own, um, eval is, it's actually relatively straightforward. We have a, a set of, of documents in a, in a range of industries. We give the agent previously did this as a one shot test of just purely the model. And then we just realized we, we need to, based on where everything's going, it's just gotta be more agentic.So now it's a bit more of a test of both our harness and the model. And we have a rubric of a set of things that has to get right and we score it. Um, and you're just seeing, you know, these incredible jumps in almost every single model in its own family of, you know, opus four, um, you know, sonnet four six versus sonnet four five.swyx: Yeah. We have this up on screen.Aaron Levie: Okay, cool. So some, you're seeing it somewhere like. I, I forget the to, it was like 15 point jump, I think on the main, on the overall,swyx: yes.Aaron Levie: And it's just like, you know, these incredible leaps that, that are starting to happen. Um,swyx: and OP doesn't know any, like any, it's completely held out from op.Aaron Levie: This is not in any, there's no public data which has, you know, Ben benefits and this is just a private eval that we [00:33:00] do, and then we just happen to show it to, to the world. Hmm. So you can't, you can't train against it. And I think it's just as representative of. It's obviously reasoning capabilities, what it's doing at, at, you know, kind of test time, compute capabilities, thinking levels, all like the context rot issues.So many interesting, you know, kind of, uh, uh, capabilities that are, that are now improvingswyx: one sector that you have. That's interesting.Industries and Datasetsswyx: Uh, people are roughly familiar with healthcare and legal, but you have public sector in there.Aaron Levie: Yeah.swyx: Uh, what's that? Like, what, what, what is that?Aaron Levie: Yeah, and, and we actually test against, I dunno, maybe 10 industries.We, we end up usually just cutting a few that we think have interesting gains. All extras, won a lot of like government type documents. Um,swyx: what is that? What is it? Government type documents?Aaron Levie: Government filings. Like a taxswyx: return, likeAaron Levie: a probably not tax returns. It would be more of what would go the government be using, uh, as data.So, okay. Um, so think about research that, that type of, of, of data sets. And then we have financial services for things like data rooms and what would be in an investment prospectus. Uhhuh,swyx: that one you can dog food.Aaron Levie: Yeah, exactly. Exactly. Yes. Yes. [00:34:00] So, uh, so we, we run the models, um, in now, you know, more of an agent mode, but, but still with, with kinda limited capacity and just try and see like on a, like, for like basis, what are the improvements?And, and again, we just continue to be blown away by. How, how good these models are getting.swyx: Yeah, I mean, I think every serious AI company needs something like that where like, well, this is the work we do. Here's our company eval. Yeah. And if you don't have it, well, you're not a serious AI company.Aaron Levie: There's two dimensions, right?So there's, there's like, how are the models improving? And so which models should you either recommend a customer use, which one should you adopt? But then every single day, we're making changes to our agents. And you need to knowswyx: if you regressed,Aaron Levie: if you know. Yeah. You know, I've been fully convinced that the whole agent observability and eval space is gonna be a massive space.Um, super excited for what Braintrust is doing, excited for, you know, Lang Smith, all the things. And I think what you're going to, I mean, this is like every enter like literally every enterprise right now. It's like the AI companies are the customers of these tools. Every enterprise will have this. Yeah, you'll just [00:35:00] have to have an eval.Of all of your work and like, we'll, you'll have an eval of your RFP generation, you'll have an eval of your sales material creation. You'll have an eval of your, uh, invoice processing. And, and as you, you know, buy or use new agentic systems, you are gonna need to know like, what's the quality of your, of your pipeline.swyx: Yeah.Aaron Levie: Um, so huge, huge market with agent evals.swyx: Yeah.Building the Agent Teamswyx: And, and you know, I'm gonna shout out your, your team a bit, uh, your CTO, Ben, uh, did a great talk with us last year. Awesome. And he's gonna come back again. Oh, cool. For World's Fair.Aaron Levie: Yep.swyx: Just talk about your team, like brag a little bit. I think I, I think people take these eval numbers in pretty charts for granted, but No, there, I mean, there's, there's lots of really smart people at work during all this.Aaron Levie: Biggest shout out, uh, is we have a, we have a couple folks at Dya, uh, Sidarth, uh, that, that kind of run this. They're like a, you know, kind of tag tag team duo on our evals, Ben, our CTO, heavily involved Yasha, head of ai, uh, you know, a bunch of folks. And, um, evals is one part of the story. And then just like the full, you know, kind of AI.An agent team [00:36:00] is, uh, is a, is a pretty, you know, is core to this whole effort. So there's probably, I don't know, like maybe a few dozen people that are like the epicenter. And then you just have like layers and layers of, of kind of concentric circles of okay, then there's a search team that supports them and an infrastructure team that supports them.And it's starting to ripple through the entire company. But there's that kind of core agent team, um, that's a pretty, pretty close, uh, close knit group.swyx: The search team is separate from the infra team.Aaron Levie: I mean, we have like every, every layer of the stack we have to kind of do, except for just pure public cloud.Um, but um, you know, we, we store, I don't even know what our public numbers are in, you know, but like, you can just think about it as like a lot of data is, is stored in box. And so we have, and you have every layer of the, of the stack of, you know, how do you manage the data, the file system, the metadata system, the search system, just all of those components.And then they all are having to understand that now you've got this new customer. Which is the agent, and they've been building for two types of customers in the past. They've been building for users and they've been building for like applications. [00:37:00] And now you've got this new agent user, and it comes in with a difference of it, of property sometimes, like, hey, maybe sometimes we should do embeddings, an embedding based, you know, kind of search versus, you know, your, your typical semantic search.Like, it's just like you have to build the, the capabilities to support all of this. And we're testing stuff, throwing things away, something doesn't work and, and not relevant. It's like just, you know, total chaos. But all of those teams are supporting the agent team that is kind of coming up with its requirements of what, what do we need?swyx: Yeah. No, uh, we just came from, uh, fireside chat where you did, and you, you talked about how you're doing this. It's, it's kind of like an internal startup. Yeah. Within the broader company. The broader company's like 3000 people. Yeah. But you know, there's, there's a, this is a core team of like, well, here's the innovation center.Aaron Levie: Yeah.swyx: And like that every company kind of is run this way.Aaron Levie: Yeah. I wanna be sensitive. I don't call it the innovation center. Yeah. Only because I think everybody has to do innovation. Um, there, there's a part of the, the, the company that is, is sort of do or die for the agent wave.swyx: Yeah.Aaron Levie: And it only happens to be more of my focus simply because it's existential that [00:38:00] we get it right.swyx: Yeah.Aaron Levie: All of the supporting systems are necessary. All of the surrounding adjacent capabilities are necessary. Like the only reason we get to be a platform where you'd run an agent is because we have a security feature or a compliance feature, or a governance feature that, that some team is working on.But that's not gonna be the make or break of, of whether we get agents right. Like that already exists and we need to keep innovating there. I don't know what the right, exact precise number is, but it's not a thousand people and it's not 10 people. There's a number of people that are like the, the kind of like, you know, startup within the company that are the make or break on everything related to AI agents, you know, leveraging our platform and letting you work with your data.And that's where I spend a lot of my time, and Ben and Yosh and Diego and Teri, you know, these are just, you know, people that, that, you know, kind of across the team. Are working.swyx: Yeah. Amazing.Read Write Agent WorkflowsJeff Huber: How do you, how do you think about, I mean, you talked a lot about like kinda read workflows over your box data. Yep.Right. You know, gen search questions, queries, et cetera. But like, what about like, write or like authoring workflows?Aaron Levie: Yes. I've [00:39:00] already probably revealed too much actually now that I think about it. So, um, I've talked about whatever,Jeff Huber: whatever you can.Aaron Levie: Okay. It's just us. It's just us. Yeah. Okay. Of course, of course.So I, I guess I would just, uh, I'll make it a little bit conceptual, uh, because again, I've already, I've already said things that are not even ga but, but we've, we've kinda like danced around it publicly, so I, yeah, yeah. Okay. Just like, hopefully nobody watches this, um, episode. No.swyx: It's tidbits for the Heidi engaged to go figure out like what exactly, um, you know, is, is your sort of line of thinking.Sure. They can connect the dots.Aaron Levie: Yeah. So, so I would say that, that, uh, we, you know, as a, as a place where you have your enterprise content, there's a use case where I want to, you know, have an agent read that data and answer questions for me. And then there's a use case where I want the agent to create something.And use the file system to create something or store off data that it's working on, or be able to have, you know, various files that it's writing to about the work it's doing. So we do see it as a total read write. The harder problem has so far been the read only because, because again, you have that kind of like 10 [00:40:00] million to one ratio problem, whereas rights are a lot of, that's just gonna come from the model and, and we just like, we'll just put it in the file system and kinda use it.So it's a little bit of a technically easier problem, but the only part that's like, not necessarily technically hard, it is just like it's not yet perfected in the state of the ecosystem is, you know, building a beautiful PowerPoint presentation. It's still a hard problem for these models. Like, like we still, you know, like, like these formats are just, we're not built for.They'reswyx: working on it.Aaron Levie: They're, they're working on it. Everybody's working on it.swyx: Every launch is like, well, we do PowerPoint now.Aaron Levie: We're getting, yeah, getting a lot, getting a lot of better each time. But then you'll do this thing where you'll ask the update one slide and all of a sudden, like the fonts will be just like a little bit different, you know, on two of the slides, or it moved, you know, some shape over to the left a little bit.And again, these are the kind of things that, like in code, obviously you could really care about if you really care about, you know, how beautiful is the code, but at the end, user doesn't notice all those problems and file creation, the end user instantly sees it. You're [00:41:00] like, ah, like paragraph three, like, you literally just changed the font on me.Like it's a totally different font and like midway through the document. Mm-hmm. Those are the kind of things that you run into a lot of in the, in the content creation side. So, mm-hmm. We are gonna have native agents. That do all of those things, they'll be powered by the leading kind of models and labs.But the thing that I think is, is probably gonna be a much bigger idea over time is any agent on any system, again, using Box as a file system for its work, and in that kind of scenario, we don't necessarily care what it's putting in the file system. It could put its memory files, it could put its, you know, specification, you know, documents.It could put, you know, whatever its markdown files are, or it could, you know, generate PDFs. It's just like, it's a workspace that is, is sort of sandboxed off for its work. People can collaborate into it, it can share with other people. And, and so we, we were thinking a lot about what's the right, you know, kind of way to, to deliver that at scale.Docs Graphs and Founder Modeswyx: I wanted to come into sort of the sort of AI transformation or AI sort of, uh, operations things. [00:42:00] Um, one of the tweets that you, that you wanted to talk about, this is just me going through your tweets, by the way. Oh, okay. I mean, like, this is, you readAaron Levie: one by one,swyx: you're the, you're the easiest guest to prep for because you, you already have like, this is the, this is what I'm interested in.I'm like, okay, well, areAaron Levie: we gonna get to like, like February, January or something? Where are we in the, in the timelines? How far back are we going?swyx: Can you, can you describe boxes? A set of skills? Right? Like that, that's like, that's like one of the extremes of like, well if you, you just turn everything into a markdown file.Yeah. Then your agent can run your company. Uh, like you just have to write, find the right sequence of words toAaron Levie: Yes.swyx: To do it.Aaron Levie: Sorry, isthatswyx: the question? So I think the question is like, what if we documented everything? Yes. The way that you exactly said like,Aaron Levie: yes.swyx: Um, let's get all the Fortune five hundreds, uh, prepared for agents.Yes. And like, you know, everything's in golden and, and nicely filed away and everything. Yes. What's missing? Like, what's left, right? LikeAaron Levie: Yeah.swyx: You've, you've run your company for a decade. LikeAaron Levie: Yeah. I think the challenge is that, that that information changes a week later. And because something happened in the market for that [00:43:00] customer, or us as a company that now has to go get updated, and so these systems are living and breathing and they have to experience reality and updates to reality, which right now is probably gonna be humans, you know, kinda giving those, giving them the updates.And, you know, there is this piece about context graphs as as, uh, that kinda went very viral. Yeah. And I, I, I was like a, i, I, I thought it was super provocative. I agreed with many parts of it. I disagree with a few parts around. You know, it's not gonna be as easy as as just if we just had the agent traces, then we can finally do that work because there's just like, there's so much more other stuff that that's happening that, that we haven't been able to capture and digitize.And I think they actually represented that in the piece to be clear. But like there's just a lot of work, you know, that that has to, you just can't have only skills files, you know, for your company because it's just gonna be like, there's gonna be a lot of other stuff that happens. Yeah. Change over time.Yeah. Most companies are practically apprenticeships.swyx: Most companies are practically apprenticeships. LikeJeff Huber: every new employee who joins the team, [00:44:00] like you span one to three months. Like ramping them up.Aaron Levie: Yes. AllJeff Huber: that tat knowledgeAaron Levie: isJeff Huber: not written down.Aaron Levie: Yes.Jeff Huber: But like, it would have to be if you wanted to like give it to an Asian.Right. And so like that seems to me like to beAaron Levie: one is I think you're gonna see again a premium on companies that can document this. Mm-hmm. Much. There'll be a huge premium on that because, because you know, can you shorten that three month ramp cycle to a two week ramp cycle? That's an instant productivity gain.Can you re dramatically reduce rework in the organization because you've documented where all the stuff is and where the answers are. Can you make your average employee as good as your 90th percentile employee because you've captured the knowledge that's sort of in the heads of, of those top employees and make that available.So like you can see some very clear productivity benefits. Mm-hmm. If you had a company culture of making sure you know your information was captured, digitized, put in a format that was agent ready and then made available to agents to work with, and then you just, again, have this reality of like add a 10,000 person [00:45:00] company.Mapping that to the, you know, access structure of the company is just a hard problem. Is like, is like, yeah, well, you just, not every piece of information that's digitized can be shared to everybody. And so now you have to organize that in a way that actually works. There was a pretty good piece, um, this, this, uh, this piece called your company as a file is a file system.I, did you see that one?swyx: Nope.Aaron Levie: Uh, yes. You saw it. Yeah. And, and, uh, I actually be curious your thoughts on it. Um, like, like an interesting kind of like, we, we agree with it because, because that's how we see the world and, uh,swyx: okay. We, we have it up on screen. Oh,Aaron Levie: okay. Yeah. But, but it's all about basically like, you know, we've already, we, we, we already organized in this kind of like, you know, permission structure way.Uh, and, and these are the kind of, you know, natural ways that, that agents can now work with data. So it's kind of like this, this, you know, kind of interesting metaphor, but I do think companies will have to start to think about how they start to digitize more, more of that data. What was your take?Jeff Huber: Yeah, I mean, like the company's probably like an acid compliant file system.Aaron Levie: Uh,Jeff Huber: yeah. Which I'm guessing boxes, right? So, yeah. Yes.swyx: Yeah. [00:46:00]Jeff Huber: Which you have a great piece on, but,swyx: uh, yeah. Well, uh, I, I, my, my, my direction is a little bit like, I wanna rewind a little bit to the graph word you said that there, that's a magic trigger word for us. I always ask what's your take on knowledge graphs?Yeah. Uh, ‘cause every, especially at every data database person, I just wanna see what they think. There's been knowledge graphs, hype cycles, and you've seen it all. So.Aaron Levie: Hmm. I actually am not the expert in knowledge graphs, so, so that you might need toswyx: research, you don't need to be an expert. Yeah. I think it's just like, well, how, how seriously do people take it?Yeah. Like, is is, is there a lot of potential in the, in the HOVI?Aaron Levie: Uh, well, can I, can I, uh, understand first if it's, um, is this a loaded question in the sense of are you super pro, super con, super anti medium? Iswyx: see pro, I see pros and cons. Okay. Uh, but I, I think your opinion should be independent of mine.Aaron Levie: Yeah. No, no, totally. Yeah. I just want to see what I'm stepping into.swyx: No, I know. It's a, and it's a huge trigger word for a lot of people out Yeah. In our audience. And they're, they're trying to figure out why is that? Because whyAaron Levie: is this such aswyx: hot item for them? Because a lot of people get graph religion.And they're like, everything's a graph. Of course you have to represent it as a graph. Well, [00:47:00] how do you solve your knowledge? Um, changing over time? Well, it's a graph.Aaron Levie: Yeah.swyx: And, and I think there, there's that line of work and then there's, there's a lot of people who are like, well, you don't need it. And both are right.Aaron Levie: Yeah. And what do the people who say you don't need it, what are theyswyx: arguing for Mark down files. Oh, sure, sure. Simplicity.Aaron Levie: Yeah.swyx: Versus it's, it's structure versus less structure. Right. That's, that's all what it is. I do.Aaron Levie: I think the tricky thing is, um, is, is again, when this gets met with real humans, they're just going to their computer.They're just working with some people on Slack or teams. They're just sharing some data through a collaborative file system and Google Docs or Box or whatever. I certainly like the vision of most, most knowledge graph, you know, kind of futuristic kind of ways of thinking about it. Uh, it's just like, you know, it's 2026.We haven't seen it yet. Kind of play out as as, I mean, I remember. Do you remember the, um, in like, actually I don't, I don't even know how old you guys are, but I'll for, for to show my age. I remember 17 years ago, everybody thought enterprises would just run on [00:48:00] Wikis. Yeah. And, uh, confluence and, and not even, I mean, confluence actually took off for engineering for sure.Like unquestionably. But like, this was like everything would be in the w. And I think based on our, uh, our, uh, general style of, of, of what we were building, like we were just like, I don't know, people just like wanna workspace. They're gonna collaborate with other people.swyx: Exactly. Yeah. So you were, you were anti-knowledge graph.Aaron Levie: Not anti, not anti. Soswyx: not nonAaron Levie: I'm not, I'm not anti. ‘cause I think, I think your search system, I just think these are two systems that probably, but like, I'm, I'm not in any religious war. I don't want to be in anybody's YouTube comments on this. There's not a fight for me.swyx: We, we love YouTube comments. We're, we're, we're get into comments.Aaron Levie: Okay. Uh, but like, but I, I, it's mostly just a virtue of what we built. Yeah. And we just continued down that path. Yeah.swyx: Yeah.Aaron Levie: And, um, and that, that was what we pursued. But I'm not, this is not a, you know, kind of, this is not a, uh, it'sswyx: not existential for you. Great.Aaron Levie: We're happy to plug into somebody else's graph.We're happy to feed data into it. We're happy for [00:49:00] agents to, to talk to multiple systems. Not, not our fight.swyx: Yeah.Aaron Levie: But I need your answer. Yeah. Graphs or nerd Snipes is very effective nerd.swyx: See this is, this is one, one opinion and then I've,Jeff Huber: and I think that the actual graph structure is emergent in the mind of the agent.Ah, in the same way it is in the mind of the human. And that's a more powerful graph ‘cause it actually involved over time.swyx: So don't tell me how to graph. I'll, I'll figure it out myself. Exactly. Okay. All right. AndJeff Huber: what's yours?swyx: I like the, the Wiki approach. Uh, my, I'm actually

Artificial intelligence is rapidly transforming the business landscape, redefining how value is created and where human work fits within the new paradigm. Long-standing advice to amass knowledge and out-execute others is now running up against sophisticated AI agents that can process information and perform tasks at speeds and scales unattainable by humans. In this emerging era, Christopher Lochhead's insights point to a critical shift from being a traditional “knowledge worker” to embracing the future as a “creator capitalist.” On this episode, Christopher Lochhead moves over to the guest chair and answer our questions about AI, Creator Capitalists, and the future of work.  You're listening to Christopher Lochhead: Follow Your Different. We are the real dialogue podcast for people with a different mind. So get your mind in a different place, and hey ho, let's go.   Why the Knowledge Worker Playbook Is Obsolete For decades, success in business hinged on being a master of knowledge and execution. This model rewarded those who reacted effectively, put out fires, and delivered results with established frameworks. However, with AI making information and execution nearly free and instantly accessible, simply reacting and executing is no longer enough. As Christopher Lochhead argues, clinging to this outdated success formula is akin to opening a video rental store in the age of streaming services. Today, the competitive edge lies in moving upstream to activities that AI cannot easily replicate. This means focusing on judgment, unique perspectives, and the ability to define, frame, and solve new problems. Humans cannot out-execute a GPU, but they can out-create one by leveraging skills that remain distinctly human.   The Four Capitals of the Creator Capitalist Framework Lochhead's Creator Capitalist concept rests on the mastery and integration of four kinds of capital: intellectual, relationship, reputational, and financial. Intellectual capital emerges from differentiated insights, deep domain expertise, and unique perspectives. Relationship capital is built through genuine connections and trust within your network, while reputational capital is earned through tangible results and reliability, not just self-promotional branding. Bringing these capitals together creates a flywheel that drives lasting success, even as AI commoditizes old sources of value. Financial capital follows as a natural result of delivering value that others find meaningful. Those able to orchestrate these four capitals will build not just AI-resistant careers but ones supercharged by the new opportunities technology presents.   Unleashing Human Potential: Adapt, Create, and Lead As AI handles more routine tasks, the future belongs to those who cultivate curiosity, creativity, and critical thinking. These human abilities enable us to ask better questions, generate bold ideas, and envision solutions no algorithm can predict. Lochhead urges professionals to take radical responsibility for their careers and continually seek ways to create net new value. Adapting to this shift means letting go of fear and embracing the opportunity to redefine what it means to be valuable. The most successful individuals and organizations will be those who harness AI as a tool to augment their creative power and lead the way into uncharted territory. The age of the creator capitalist has arrived, and it's time to build the future together. To hear more of Christopher Lochhead’s thoughts on Creator Capitalist and the future of work, download and listen to this episode.   Links Want to catch more episode of the AI Agent & Copilot Podcast? You can check them out here: Presented by Cloud Wars | AI Agent and Copilot Podcast | John Siefert LinkedIn | Cloud Wars LinkedIn   We hope you enjoyed this episode of Christopher Lochhead: Follow Your Different™! Christopher loves hearing from his listeners. Feel free to email him, connect on Facebook, X (formerly Twitter), Instagram, and subscribe on Apple Podcast / Spotify!

➡️ Want To Learn More About Partnering With with Alan Kushmakov and myself at eXp? Schedule Your 100% Confidential, 100% Zero Pressure Discovery Call: https://calendly.com/alankbesthomesre/exp-explained   ➡️ Connect with Alan Kushmakov: Facebook: https://www.facebook.com/alankushmakov Instagram: https://www.instagram.com/alankushmakov LinkTree: https://linktr.ee/alankushmakov   ➡️ Connect With Me On Social Media: Facebook: https://www.facebook.com/JoshuaSmithGSD Instagram: https://instagram.com/joshuasmithgsd/   About Joshua Smith: -Licensed Realtor/Team Leader Since 2005 -Voted 30th Top Realtor in America by The Wall Street Journal -NAR "30 Under 30" Finalist -Named Top 100 Most Influential People In Real Estate -Top 1% of Realtors/Team Leaders Worldwide -6000+ Homes Sold & Currently Selling 1+ Homes Daily -Featured In: Forbes, Wall Street Journal, Inman & Realtor Magazine -Realtor, Team Leader, Coach, Mentor

Routstr is an open marketplace for ai compute, powered by nostr and bitcoin.Routstr: https://routstr.comChat app: https://chat.routstr.comOpenclaw setup: https://routstr.com/openclawRun a Routstr node and earn sats: https://github.com/Routstr/routstr-coreGithub: https://github.com/Routstr Routstr on nostr: https://primal.net/p/npub130mznv74rxs032peqym6g3wqavh472623mt3z5w73xq9r6qqdufs7ql29sEvan on nostr: https://primal.net/p/npub1u37h8rhgm9f95d90lpk2afw8h4t75kf6w8vmga2zz9jsx3atzpuqlmw8vyRedshift on nostr: https://primal.net/p/npub1ftt05tgku25m2akgvw6v7aqy5ux5mseqcrzy05g26ml43xf74nyqsredshThefux on nostr: https://primal.net/p/npub1ygjd597hdwu8larprmhj893d5p832j5mhejpx40ukezgudvayg9qeklajcShroominic on nostr: https://primal.net/p/npub18gr2m5cflkzpn6jdfer4a8qdlavsn334m9mfhurjsge08grg82zq6hu9suEPISODE: 192BLOCK: 939283PRICE: 1368 sats per dollar(00:03:02) Routstr and the team(00:07:24) What is Routstr?(00:10:26) Proxy providers, proprietary models, and pricing dynamics(00:13:16) Discovery, reviews, and quality signaling on Nostr(00:16:07) Fees, sustainability, and open source funding models(00:21:32) OpenClaw, LNVPS, and one-click sovereign stack(00:25:27) Why Nostr is ideal for agents vs. closed platforms(00:33:00) Crowdzapping, bounties, and agents building public goods(00:38:02) Agent specialization, cost tiers, and future routing(00:45:31) Resilience: routing around outages and pay-per-request(00:48:12) Self-host vs. marketplaces, selling spare compute(00:54:00) AI compute meets Bitcoin mining and energy realities(00:56:50) Hardware choices: Mac minis, old PCs, and VPS security(00:59:10) Linux advantage and agents removing UX friction(01:00:24) Open chat protocols, Marmot, and agentic comms(01:03:54) Acceleration, small teams with many agents shipping fast(01:04:19) Closing thoughts from the Routstr teammore info on the show: https://citadeldispatch.comlearn more about me: https://odell.xyz

The Top 5 Issues Managing Multiple AI Agents in Production Managing 1-2 AI agents? Easy. Managing 20+? That's a different game entirely. After 9+ months running nearly 30 AI agents in production at SaaStr, we've learned what actually breaks at scale - and nobody's talking about it. This isn't about deployment tips or vendor selection. This is about the brutal realities that only emerge when you're juggling 20+ agents generating $1M+ in revenue.

The Mag 7 underperforming recently, but Rosenblatt Securities sees better days ahead. Box CEO Aaron Levie says AI agents will be the biggest users of software in the future. Plus, the Korean Kospi was the best performing stock market in the world last year, but posted its worst day ever amid concerns over the Iran conflict. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

What happens when you mix 26 years of experience, private property rights, and zero tolerance for nonsense? You get Leigh Brown unfiltered. In this episode, Leigh dives into the real history of the MLS, why cooperation matters more than ever, and what newer agents are missing about how this industry was built. She breaks down universal cooperation, the danger of fragmentation, and why forgetting our past could cost consumers their access to a free and fair marketplace. But this isn't just policy and philosophy. Leigh shares tactical, practical gold on prospecting, social media, database calls, and how to stay relevant without being boring. From her eight-word phone script to her "be useful" philosophy, this is a masterclass in staying top of mind while protecting what makes real estate work. If you care about your career, your clients, and the American dream this episode is for you. Links mentioned during the episode: https://www.leighbrown.com/resources   Connect with Leigh on LinkedIn and social media at Linktree. Also, check out her podcast Real Talk.    Is your real estate business building equity or just paying the bills? At FirstTeam® Real Estate, Behind the Agent™ means putting real ownership in your hands. This isn't about stacking commissions, it's about building something that lasts.   You stay the face of your brand. We bring the strategy, marketing, leadership, and infrastructure to scale it.   No franchise caps. No growth ceilings. Just the freedom to run your business like a business. We're not building a roster. We're building real careers.   Break the glass ceiling. Own your future. Explore agent ownership opportunities at: https://firstteam.com/inman   Stay ahead of the market with insights from Zillow's Consumer Trends Report. This annual research breaks down what buyers and sellers are actually doing — and what they expect from their agents.  With 55% of buyers being repeat buyers, yet only 13% using their previous agent again, the message is clear: past performance doesn't guarantee future loyalty. Even more telling, nearly half of buyers and over half of sellers hire the first agent they contact.  Today's clients prioritize pricing strategy, negotiation expertise, and a seamless, organized offer process. If you want to position yourself as the agent they choose in 2026, start with the data.  Explore the full report and put these insights to work in your business. https://bit.ly/4rDZg3M Subscribe to Real Estate Insiders Unfiltered on YouTube! https://www.youtube.com/@RealEstateInsidersUnfiltered?sub_confirmation=1   To learn more about becoming a sponsor of the show, send us an email: jessica@inman.com   You asked for it. We delivered. Check out our new merch! https://merch.realestateinsidersunfiltered.com/   Follow Real Estate Insiders Unfiltered Podcast on Instagram - YouTube, Facebook - TikTok. Visit us online at realestateinsidersunfiltered.com.   Link to Facebook Page: https://www.facebook.com/RealEstateInsidersUnfiltered Link to Instagram Page: https://www.instagram.com/realestateinsiderspod/ Link to YouTube Page: https://www.youtube.com/@RealEstateInsidersUnfiltered Link to TikTok Page: https://www.tiktok.com/@realestateinsiderspod Link to website: https://realestateinsidersunfiltered.com This podcast is produced by Two Brothers Creative. https://twobrotherscreative.com/contact/  

SED News is a monthly podcast from Software Engineering Daily where hosts Gregor Vand and Sean Falconer unpack the biggest stories shaping software engineering, Silicon Valley, and the broader tech industry. In this episode, they cover the viral rise of OpenClaw and its founder's move to OpenAI, OpenAI's exploration of ads inside ChatGPT, and Alibaba's The post SED News: OpenClaw Goes Viral, Mistral's Compute Play, and the Agent Arms Race appeared first on Software Engineering Daily.

“Agent 99” herself, the funny and charming Barbara Feldon invites Gilbert and Frank to her New York City townhouse to share warm memories of “Get Smart” co-stars Don Adams, Ed Platt and Bernie Kopell and to give the boys her take on the Steve Carell feature film version. Also, Barbara looks back on working with everyone from Dean Martin to Bruce Dern and tells us how she managed to win $64,000 on a quiz show. Also in this episode: Gilbert channels John McGiver, Barbara auditions to be a stripper, and the worst TV movie ever made. PLUS: A live, all-new rendition of the "99" song! Learn more about your ad choices. Visit megaphone.fm/adchoices