Podcasts about career path

The next generation of financial advisors and leaders are not asking for a job, they're asking for a future. Ray Sclafani explores why career pathing has evolved from a nice-to-have benefit into the most critical retention lever advisory firms have. Drawing on Deloitte's 2025 research showing only 6% of Gen Z and Millennials prioritize reaching a leadership position, Ray unpacks what ambition actually looks like today: growth, meaning, money, well-being, and a thoughtful pace of development.For advisory firm owners and leaders, the implications are direct. A firm with no clear development path doesn't stand still, it falls behind. This episode provides a five-part framework for building career pathways that work. Ray then shares a practical starting point: a single career conversation in the next 60 to 90 days that changes how your people feel about their future with your firm.The firms that provide honest visibility of a future worth building will retain more top talent, develop better leaders, and build more durable businesses.WHAT YOU'LL LEARN IN THIS EPISODEWhy the next generation defines ambition differently and what that means for retention strategyHow to define roles with clarity and purpose so every position has a visible pathwayThe single biggest mistake firms make when building career paths and how to avoid itWhy addressing AI's role impact directly is now a core part of career developmentHow to eliminate ambiguity around partnership so people stop guessing what it meansTHE FIVE-PART CAREER PATHING FRAMEWORKDefine the Roles. Establish clear purpose, expectations, and required skills for each position. Map progression pathways for advisors (from client service associate to enterprise leader), operations (specialist to enterprise operator), and leadership (people manager to executive leader).Define the Progression. Specify what it takes to move from one role to the next: technical skills, client relationship management, leadership capabilities, business development expectations, decision rights, and cultural behaviors. Specificity builds trust.Connect to Actual Development. Attach real development objectives to each progression step. Identify specific competencies that need improvement, not vague hopes. The manager's job is connecting today's work to tomorrow's opportunity.Address AI's Impact. Clarify which skills become more valuable (empathy, judgment, planning, decision making, communication, relationship leadership) and commit to training people to use AI responsibly. Don't let people wonder alone.Make Ownership Expectations Clear. Define passages to partnership, distinguish between producing and nonproducing partners, clarify income versus equity partnership, and spell out what business development, client retention, leadership, and enterprise thinking mean for ownership.REFLECTION QUESTIONS FOR YOUR LEADERSHIP TEAMCan every high potential employee at your firm see a future worth working toward?Where are career paths clearly defined, and where are they implied but not yet documented?Which roles will AI reshape first? And how are you preparing your team for that shift?Who needs a development conversation before they start taking calls from another firm?RESOURCES MENTIONEDDeloitte 2025 Gen Z and Millennial SurveySchwab 2025 Career Pathing ResearchCFP Board Career Pathway ResourcesClientWise Business Builders Academy™ClientWise Executive Coaching and Team DevelopmentBuilding the Billion Dollar Business is hosted by Ray Sclafani, founder and CEO of ClientWise, the financial services industry's leading executive coaching and team development firm for elite advisors and wealth management teams.Find Ray and the ClientWise Team on the ClientWise website or LinkedIn | Twitter | Instagram | Facebook | YouTubeBuilding The Billion Dollar Business

Lieutenant-Colonel Chris Bray is the Commanding Officer of 427 Special Operations Aviation Squadron, the Royal Canadian Air Force unit supporting the Canadian Special Operations Forces Command (CANSOFCOM).In Part 2, we take a rare look behind the curtain of Canadian Special Operations Aviation, exploring how 427 Squadron evolved into the modern capability it is today. Chris explains the squadron's culture, what makes it unique among allied SOF aviation units, the philosophy of "no mission too small," the addition of the CE-145C Vigilance, and the standards that define one of Canada's most demanding aviation communities. We also discuss leadership, mission command, and how 427 built a reputation for professionalism, adaptability, and trust while supporting special operations around the world.CONTINUE THE FLIGHTEXPLORE THE LOGBOOKMore stories from RCAF and mission aviation pilotshttps://podpilotproject.transistor.fm/episodesCHAPTERS(00:00:00) Introduction and Welcome(00:02:41) How 427 Squadron Reinvented Itself(00:03:39) Building One Special Operations Aviation Culture(00:06:49) Why the Squadron Had to Change(00:09:44) Living Through the Transformation(00:13:10) Earning a Reputation in Special Operations Aviation(00:14:49) "No Mission Too Small"(00:18:46) Expanding the Mission with the CE-145C Vigilance(00:20:44) Learning to Think Beyond Helicopters(00:22:53) What Makes 427 Different?(00:26:21) Becoming an Organic CANSOFCOM Capability(00:28:44) The Professionals Behind Every Mission(00:30:04) Why Communications Can Make or Break a Mission(00:31:12) Building Trust with Canada's Special Operations Forces(00:33:44) Maintaining Elite Standards(00:37:52) Rewriting the Rules: The New TOCA(00:41:35) The Career Path of a Special Operations Aviator(00:44:57) Balancing Risk and Mission Success(00:46:22) Leading an Elite Squadron(00:47:34) The Hardest Decisions as Commanding Officer(00:49:57) Next Time: Inside SOTAC

In this episode, Pierre Michiels interviews John Rangel. John Rangel is a motion picture and television professor at the College of DuPage as well as an independent filmmaker. In the interview, they discuss the film and video program, the wide range of career paths available in the industry, and the key skills needed for success, including networking, creativity, and hands-on experience. They also highlight the importance of getting involved on film sets, building a portfolio, and leveraging internships to break into the field. After listening to this episode, we hope you have a better understanding of careers in the film and video industry and how to get started. Full episode transcript can be found on the episode page. Below is a general timestamp summary.  00:00–03:00 | Introduction & Program Overview Pierre Michiels introduces the episode and guest John Rangel, a motion picture and television professor and independent filmmaker. They outline the purpose of exploring the film and video field and begin discussing the College of DuPage program, which provides a broad foundation in filmmaking through exposure to directing, writing, producing, and more.  03:00–08:00 | Career Paths & Industry Roles The conversation highlights the wide range of career opportunities within film and video, emphasizing how many specialized roles exist, as seen in film credits. Rangel explains that students can pursue paths in production, videography, or related industries, while Michiels stresses the importance of recognizing these diverse options.  08:00–12:00 | Key Skills for Success Rangel identifies essential skills, including collaboration, networking, and consistent content creation. He emphasizes that success depends not just on technical ability but also on engaging with others, building connections, and actively sharing work to gain visibility.  12:00–15:30 | Gaining Experience & Internships They discuss the value of hands-on experience through working on film sets, stepping outside comfort zones, and pursuing internships. Internships are framed as critical entry points, often leading directly to job opportunities through relationships and demonstrated skills.  15:30–18:30 | Program Preparation & Student Opportunities Rangel explains how the program prepares students through frequent project work, resume and demo reel development, and a film festival that simulates real industry experiences. These opportunities help students build portfolios and confidence.  18:30–End | Final Advice & Resources The episode concludes with advice to focus on storytelling over equipment, watch diverse films, and create complete, polished projects. Rangel shares how students can learn more about the program, and the episode wraps with key takeaways on entering the field.  Resources Discussed: College of DuPage Film & Video Program webpage Contact: rangell@cod.edu for program questions Listeners in the College of DuPage community can visit our website. All other listeners are encouraged to view the resources of their local community college, WIOA training programs, or other local support centers.Send us YOUR Listener Questions at careerpodcast@cod.edu Follow us on Instagram, Facebook, Twitter, LinkedIn @codcareercenter

Curious about a flexible, high-paying nonclinical career that still uses your medical expertise? In this episode, I'm joined by Dr. Keagen Hadley, an occupational therapist who built a successful career in regulatory medical writing without ever stepping into clinical practice. Dr. Hadley shares how he discovered this little-known field while working in clinical research, the persistence it took to break in, and why regulatory writing can be such an appealing option for physicians seeking flexibility, meaningful work, and strong earning potential. We also explore what regulatory writers actually do, who tends to thrive in this role, how AI is impacting the profession, and practical strategies for getting started. If you enjoy structure, attention to detail, and helping bring new therapies to patients, this conversation may open your eyes to an exciting career path you hadn't considered. In this episode we're talking about: What regulatory medical writing is and its role in drug, biologic, and device development The personality traits and skills that help people succeed in regulatory writing Dr. Keagen Hadley's unconventional path from occupational therapy to a thriving writing career How physicians can break into the field through networking, recruiters, and strategic positioning Compensation, flexibility, and career growth opportunities in regulatory writing How AI is influencing regulatory writing and why human expertise remains essential Resources, training recommendations, and practical advice for exploring this career path You can find the show notes for this episode and more information by clicking here: www.doctorscrossing.com/episode251 Links for this episode: The Clinician's Guide to Regulatory Writing | Keagen Hadley | Substack  Keagen Hadley | LinkedIn  Targeted Regulatory Writing Techniques: Clinical Documents for Drugs and Biologics by Wood and Foote 6 Month Road Map to Becoming a Regulatory Writer by Keagen Hadley Regulatory Medical Writing Masterclass  Medical Writing Resource Guide – This 11-page guide gives you an introduction to medical writing as well as links for courses, books, websites, and tips for exploring this diverse area. Includes steps you can take whether you want to do medical writing as a side gig, or work full-time as a freelancer or an employee. Land Your Dream Job With LinkedIn: 5 Steps To Get Started – Following these 5 tips will help you optimize your profile, connect and network with others, and have success applying to jobs on LinkedIn! Thank you for listening!

Your best person is sitting in their role right now doing excellent work. And somewhere in the back of their mind, whether on the commute home, in the quiet after a long meeting, or in the gap between one task and the next — they're asking a question they haven't said out loud to you yet. Where does this actually go? Not philosophically. Practically. What is the organization doing — specifically, deliberately — to help them reach it? In most organizations, the honest answer to that question is silence. Not because the leader doesn't care. Because nobody ever drew the map. And your best people won't wait indefinitely for a map that never arrives.

This week, Chris Caplice is joined by Jim Filter, Schneider's newly appointed President and CEO, to talk about what it means to step into that role during a period of major change for the freight industry.Jim reflects on his nearly 30-year career at Schneider, the operational and international experiences that shaped his leadership, and how the company is adapting as customer needs, technology, and market conditions continue to shift.In this conversation, they cover:What it means to lead Schneider as only the fifth CEO in the company's historyHow dedicated transportation and brokerage have become bigger parts of Schneider's strategyWhat regulatory enforcement and broker liability could mean for capacityHow driver roles, tools, and career paths continue to evolveThe future of electric and autonomous trucksHow Schneider is using AI and technology across the businessIf you work in shipping, brokerage, or carrier operations, this is a timely look at how one of the industry's largest operators is thinking about the road ahead.For previous episodes and to subscribe to our newsletter, visit: dat.com/podcast/freightvine Chapters00:00 Introduction 03:36 Career Path, Leadership Preparation and Developing Talent07:29 Technological Advancements in Transportation14:13 Customer Relations and Problem Solving14:50 Current Market Dynamics and Capacity Challenges16:35 Impact of Regulations on the Industry23:17 Chameleon Carriers and Industry Challenges28:21 CEO Priorities and Strategic Focus31:18 Evolution of Dedicated Services35:59 EVs and Avs45:38 Legacy and Leadership in the Industry48:06 Freightvine Truckload Market Update48:11 Dry Van Market Update49:04 Temp Control (Reefer) Market Update49:37 Intermodal Market Update50:09 Flatbed Market Update50:56 Key Takeaways

Many people in this country don't have access to reliable financial guidance — so they're increasingly asking a free chatbot instead. The problem? A general-purpose model trained on Reddit threads inherits Reddit's appetite for risk, and now that skew shows up in the conversations people are having about their money. In this new episode of One Vision Podcast, Alisha Chowdhury, Founder of Kiro Money, joins Theodora Lau to argue that the same technology, pointed with different intentions, can do the opposite: close the advice gap instead of widening it.Alisha traces the origin of Kiro back to a socioeconomically diverse Bangladeshi community in New Orleans — the "aunties and uncles" who taught her immigrant parents how to navigate an unfamiliar financial system — and to the University of Pennsylvania, where she saw real wealth and learned how it's built for the first time. After years as an investor at Vanguard and in private equity, she left for an MBA in London and started building the earliest version of Kiro: a low-code, RAG-based coach fed only sources she trusted. "Not garbage in."Today Kiro is an embedded financial intelligence layer that lets a bank or investing platform drop a context-aware AI coach directly into its own app, so users get guidance where their data and their relationship already live. A conversation about financial inclusion, the advice gap, and what it takes to build AI for money that people can actually trust. Because when it comes to money, trust has to be earned — and tested.

Did you know Romesh Ranganathan was a maths teacher before pursuing stand-up full time.Wanda Sykes worked as a contracting specialist with top-secret security clearance at the NSA up until the 90s before quitting to pursue stand-up full-time.Moving from comedy to music. Before making it big, John Legend worked as a management consultant.You may be thinking, this is The SEO Mindset Podcast, why are you telling us these random facts?Well, welcome to season 18 (can't quite believe that) and we're changing the main theme of our podcast… Joking! Please don't be alarmed.To kick this new season off, we're talking about how there's no such thing as a linear career path, and I wanted to start with some strong evidence.My examples from those famous folk shows how career paths can change and pivot, and that is exactly what me and Tazmin will be getting into.We'll be giving examples from our own career paths. Sarah actually studied dance at university. How did she get from there to being a podcast host, producer and marketing consultant? Well you'll have to listen to this week's episode!Of course, there will be practical tips that you can implement to help you navigate and build a portfolio career.About 'The SEO Mindset' PodcastBuild your inner confidence and thrive.The SEO Mindset is a weekly podcast that will give you actionable tips, guidance and advice to help you not only build your inner confidence but to also thrive in your career.Each week we will cover topics specific to careers in the SEO industry but also broader topics too including professional and personal development.Your hosts are Life Coach Tazmin Suleman and SEO Manager Sarah McDowell, who between them have over 20 years of experience working in the industry.Get in touchWe'd love to hear from you. We have many ways that you can reach out to us to say hello, ask a question, or suggest a topic for us to discuss on a future episode.Follow us on InstagramFollow us on LinkedInSend us an emailCheck out Tazmin's WebsiteCheck out Sarah's WebsiteClick here to download your copy of our free 'Growth versus Fixed Mindset' ebook.Click here to sign up for our newsletter to receive news and updates from the podcast eg latest episodes, events, competitions etc. We will never spam and you can unsubscribe at anytime.Subscribe and never miss an episode: Listen to The SEO Mindset PodcastMentioned in this episodePrevisible helps brands growSearch has changed but most strategies haven't.If you're still measuring success purely on rankings and clicks, you're missing where real decisions are happening.Previsible helps brands understand and grow their visibility across AI platforms so you're not just present, you're chosen.If you want to see where your brand stands and how to build trust in AI-driven search today.

Hi Parents! Helping your teen choose a career path can feel like a lot of pressure, especially when you just want them to be successful, secure, and happy. But what if some of the ways we try to help are making them feel more anxious and stuck? In this episode, I sit down with Miriam Groom, Career Therapist and founder of Mindful Career, to talk about the biggest mistake parents make when guiding their teens through career decisions. We explore why so many teens feel overwhelmed when thinking about their future, and how pressure to choose the “right” or “safe” path can disconnect them from who they truly are. Miriam shares how personality, values, and self-awareness play a critical role in career alignment, and why overlooking these factors can lead to confusion, avoidance, or lack of motivation. We also talk about how to recognize whether your teen is genuinely feeling lost versus appearing unmotivated, and what parents can do differently to support them without adding pressure. If you've ever found yourself worrying about your teen's future or unsure how to guide them, this episode will give you practical, grounded strategies to help them move forward with more clarity and confidence. NEW: My 6-week parent program, The Parent's Anxiety Toolkit™, is now open! If you're raising an anxious teen and feeling overwhelmed, unsure what to say, or exhausted by the constant worry, this program will help you feel more calm, confident, and equipped with practical tools that actually work. Big hugs, Monica Crnogorac Next Steps Book a Free Discovery Call Visit My Website for More Information on My 8-Week Program for Teens Connect With Me on Instagram

In this live episode, I met with Nick Tucker and Brian Thibault. Nick has been in the elevator industry for over 10 years and is a licensed elevator mechanic with experience in service, repair, modernization, and commercial operations. He currently serves as the Commercial Operations Manager at South Jersey Elevator. In addition to his operational role, Nick is a CET instructor. Brian is the Vice President of Education and Training at Associated Builders and Contractors New Jersey. Since entering the construction industry in 2002, he has advanced through nearly every level of the profession. Now, as a licensed electrician, secretary, and instructor for the New Jersey Electrical Group, Brian is dedicated to developing the future of the skilled trades. In this conversation, we dove into one of America's only non-union elevator apprenticeship programs, and how it can give young people more access to the elevator trade.Chapters:00:00 Introduction00:03 The Workforce Crisis in Construction02:00 Misconceptions About Skilled Trades04:48 The Importance of Apprenticeship Programs07:59 Corporate Investment in Workforce Development10:41 Nick's Career Journey in Elevators14:00 Brian's Career Path and ABC's Mission16:51 The Role of ABC in Workforce Development19:40 Finding Opportunities in Skilled Trades22:41 Elevator Apprenticeship Program Development25:52 Training Structure and Safety Protocols29:52 Skills for Modern Apprentices33:50 Opportunities After Completing Apprenticeship36:59 Starting an Elevator Apprenticeship Program39:53 Innovation in the Elevator Industry40:46 Recruiting for Apprenticeship Programs42:46 Apprenticeship Enrollment Process45:54 Middle School Construction Camps46:51 Rapid Fire Questions and Insights51:46 Future of Workforce Development and ApprenticeshipsResources: Subscribe: https://www.youtube.com/@elevatorcareers/ Submit a Topic Idea for the Podcast: https://elevatorcareers.net/ Connect With Us: linktr.ee/AllredGroupA Message From Our Sponsor: Looking for top-tier talent to join your team? Call The Allred Group for your elevator recruiting needs! With a deep network and unmatched industry expertise, we quickly connect you with skilled professionals who are ready to elevate your team. Let us handle the hiring process, so you can focus on growing your business with the best in the industry. Reach out today, and let us help you take your business to new heights! To contact us go to: https://allredgroup.com

We'd love to hear from you. Send us fan mail!If you've been powering through your workday instead of actually leading it, this episode will change how you think about your next move. Bernadette Boas sits down with career strategy and executive coach Randi Roberts to talk about what it really takes to take control of your career path starting with the moment Randi turned down a company-recommended assignment to protect what mattered most to her family.Randi shares how she rebuilt her own career like a strategic plan, why suggestions from leadership are not commands, and the small trusted circle she calls her "Holy Smokes List" when she genuinely doesn't know what to do next.What You Will LearnHow to tell the difference between a leader's suggestion and a required decisionWhy rehearsing a hard career conversation before you have it changes the outcomeHow to build a personal board of directors for your careerWhat a Holy Smokes List is and why every leader needs oneThe first question to ask when a role stops fittingHow to think like "future you" when evaluating an opportunityEpisode Chapters00:00 – Cold Open: Are You Just Powering Through? 01:00 – Randi's Own Career Demotivation Story Begins 03:00 – Turning Down The Expat Assignment For Family 04:00 – The Real Risks Of Pushing Back On Leadership 06:00 – Rehearse The Hard Conversation Before You Have It 08:00 – Suggestions, Not Commands: Getting In The Driver's Seat 10:00 – Building Your Career Like A 25-Year Strategic Plan 13:00 – Think Like Future-You, Future-CEO-You 14:00 – The Pivotal Moment Randi Left Corporate 16:00 – Step One: Figure Out What's Itching 17:00 – Building Your Career Board Of Directors 20:00 – The Holy Smokes List, Explained 21:00 – Asking For Help Without Overthinking It 25:00 – Randi's Free Right Time Guide 26:00 – Close & Recap With BernadetteAbout the GuestRandi Roberts is a career strategy and executive coach who spent 30 years leading in corporate before launching her own coaching business nearly a decade ago. She helps professionals build intentional, strategic career paths instead of drifting through them.  Learn more: https://www.corlinroberts.net/Related Episodes    How to Handle Workplace Disputes Before They Become Lawsuits — with Felicia Harris Hoss    Employee Engagement Strategies That Actually Move the Needle with IAN WATTS    Define What Winning Looks Like and Watch Performance Shift with JACKSON LYNCH Subscribe CTAEnjoyed this conversation? Subscribe to Shedding the Corporate Bitch on any podcast streaming platform or YouTube @ShedtheCorpBitchTV and never miss an episode.Support the show

In this episode, Pierre Michiels interviews Tim Genc, an Associate Professor of Aviation at the College of DuPage. Tim shares his unique journey into aviation and his experience building the college's aviation program. In the interview, they discuss the wide range of career paths within aviation beyond piloting, common misconceptions about entering the field, the skills and mindset needed for success, and how students can gain exposure through networking, events, and hands-on learning opportunities. They also highlight how the program prepares students for both flying and non-flying roles while emphasizing career readiness and industry connections. After listening to this episode, we hope you have a better understanding of the diverse opportunities and pathways available within the aviation industry. Full episode transcript can be found on the episode page. Below is a general timestamp summary.  00:00 – 03:00 | Introduction & Guest Background Pierre introduces the episode and welcomes Tim, who shares his unconventional path into aviation, transitioning from discovering a passion for flying to a long career in instruction and eventually higher education. 03:00 – 06:00 | Breaking Into Aviation The conversation explores common misconceptions about entering aviation, including the belief that military experience is required, and highlights how the field is becoming more accessible through civilian pathways and education programs.  06:00 – 08:30 | Career Paths in Aviation Tim outlines the wide variety of roles in aviation—beyond pilots—including maintenance, air traffic control, and business roles, while emphasizing the growing global demand for aviation professionals. 08:30 – 12:00 | Skills & Getting Involved They discuss key traits for success, such as discipline, teamwork, and strong study habits, along with actionable ways to explore the field through organizations, events, and networking opportunities.  12:00 – 18:00 | Program Overview & Student Preparation Tim explains how the College of DuPage aviation program prepares students through hands-on learning, exposure to multiple career tracks, and professional skill development including interviewing and networking.  18:00 – 24:00 | Career Readiness & Final Advice The episode concludes with advice on building connections, exploring different pathways, and starting early engagement in aviation, reinforcing the importance of persistence and curiosity in career development. Resources Discussed: College of DuPage Aviation Program Contact Tim Genc: genct@cod.edu Listeners in the College of DuPage community can visit our website. All other listeners are encouraged to view the resources of their local community college, WIOA training programs, or other local support centers.Send us YOUR Listener Questions at careerpodcast@cod.edu Follow us on Instagram, Facebook, Twitter, LinkedIn @codcareercenter

Many people in this country don't have access to reliable financial guidance — so they're increasingly asking a free chatbot instead. The problem? A general-purpose model trained on Reddit threads inherits Reddit's appetite for risk, and now that skew shows up in the conversations people are having about their money. In this new episode of One Vision Podcast, Alisha Chowdhury, Founder of Kiro Money, joins Theodora Lau to argue that the same technology, pointed with different intentions, can do the opposite: close the advice gap instead of widening it.Alisha traces the origin of Kiro back to a socioeconomically diverse Bangladeshi community in New Orleans — the "aunties and uncles" who taught her immigrant parents how to navigate an unfamiliar financial system — and to the University of Pennsylvania, where she saw real wealth and learned how it's built for the first time. After years as an investor at Vanguard and in private equity, she left for an MBA in London and started building the earliest version of Kiro: a low-code, RAG-based coach fed only sources she trusted. "Not garbage in."Today Kiro is an embedded financial intelligence layer that lets a bank or investing platform drop a context-aware AI coach directly into its own app, so users get guidance where their data and their relationship already live. A conversation about financial inclusion, the advice gap, and what it takes to build AI for money that people can actually trust. Because when it comes to money, trust has to be earned — and tested.

In matric and still unsure which career path to follow? Guest: Bilal Kathrada, Lecturer & author by Radio Islam

What happens when the role that once fit perfectly starts feeling a little too small?In this episode, Tracy sits down with Beth Welch, a PA whose career journey has taken her from trauma and critical care medicine into executive healthcare leadership, innovation, and systems-level impact.Together, they explore what it feels like to step away from traditional clinical practice, navigate the identity shifts that come with career growth, and embrace opportunities that don't always fit the expected path.They discuss:Why careers are more like jungle gyms than laddersThe hidden leadership skills clinicians develop every dayInfluence without authorityBurnout and career evolutionThe transition from bedside clinician to executive leaderSystems thinking and healthcare innovationFinding purpose beyond a job titleHow clinicians can create impact at scaleWhether you're considering a non-clinical role, exploring entrepreneurship, or simply feeling called toward something bigger, this conversation offers both practical insights and permission to evolve.Key takeaways1. Roles Are Static. People Are Dynamic. Many clinicians experience discomfort not because they're in the wrong profession, but because they've grown beyond the role they're currently occupying.2. PAs Are Built for Leadership Adaptability, communication, systems thinking, collaboration, and problem-solving are skills clinicians practice every day—even when they don't recognize them as leadership skills.3. Influence Doesn't Require Authority. Some of the most impactful leaders aren't the people with the highest title—they're the people who consistently move projects, people, and ideas forward.4. Burnout can be a Positive Catalyst For many clinicians, burnout becomes the signal that something needs to change—not necessarily medicine itself, but how they engage with it.5. Career Paths are Rarely Linear The most fulfilling careers often look more like a jungle gym than a ladder, with lateral moves, pivots, experiments, and unexpected opportunities along the way.Bethany Welch http://bethanywelch.com/ Clinician Entrepreneur Collective www.tracybingaman.com/waitlist

No one ever told me that I could grow up to be a nun who plays ultimate frisbee in the summer AND has a podcast... I feel like I've really missed an opportunity hereSubscribe on LiSTNR: https://play.listnr.com/podcasts/carrie-and-tommySee omnystudio.com/listener for privacy information.

Kaitlyn Carlson from Theory Planning Partners joins Carrie Kerpen for a powerful conversation about money, values, and preparing for the exit of your dreams. From surviving toxic power dynamics in wealth management to building a financial planning firm rooted in honesty and advocacy, Kaitlyn breaks down what founders should be thinking about long before they sell. This episode is sponsored by Theory Planning Partners. Theory Planning Partners helps founders and business owners make smarter decisions around wealth, financial planning, and life after exit. Learn more at www.theoryplanning.com.4:13 - Kaitlyn's Background & Ice Hockey 6:38 - Career Path into Finance 13:33 - The AUM Model Explained 23:58 - Mindset: Abundance vs. Scarcity 26:53 - Theory Planning Partners' Flat Fee Model 33:26 - When to Start Planning Your Exit 35:52 - Investment Categories & Shiny Objects 42:25 - Advice for the First-Time Founder

What if the reason you feel restless, scattered, or like you can't stick to one thing isn't a flaw — but actually a feature? What if the conventional career path was never designed for you in the first place?In this episode, Kathryn sits down with integrative career coach Alex Rizzi to explore why so many driven, intuitive, and creative people feel suffocated by the "pick a lane" narrative — whether that's the 9-to-5 grind or the "one signature offer" entrepreneurship model. Together they unpack the concept of a portfolio career, the internal deconditioning required to step into it, and why chasing certainty and guaranteed outcomes might actually be what's keeping you stuck in scarcity.BY THE TIME YOU FINISH LISTENING TO THIS EPISODE, YOU WILL DISCOVER:● What a portfolio career actually is — a dynamic, self-designed combination of creative work, contract roles, entrepreneurship, and short or long-term projects — and why more people are being called toward this non-linear path as traditional employment systems continue to crumble around them.● How the relentless pursuit of certainty and one-track career thinking can quietly keep you locked in a scarcity mindset, while releasing the death grip on "one right way" often creates the space for wildly unexpected, aligned opportunities to flow in almost magically.● Why career and business strategy is only truly available after doing the internal landscape work first — unpacking inherited beliefs, borrowed stories, and unconscious conditioning that run silently in the background, sabotaging even the most well-crafted plans before they have a chance to take hold.Connect with Alex here:Alex Rizzi is an integrative career coach helping highly sensitive professionals navigate career transitions with clarity and confidence. Drawing on her background in psychotherapy, hypnotherapy, holistic career coaching and somatic work, Alex supports clients in breaking free from corporate burnout and reclaiming their unique, authentic path.Website: alexrizzicoaching.comLinkedIn: https://www.linkedin.com/in/alexandrarizzi/Instagram: https://www.instagram.com/alexrizzicoaching/And while you're here, follow us on Instagram @creativelyowned for more daily inspiration on effortlessly attracting the most aligned clients without spending hours marketing your business or chasing clients. Also, make sure to tag me in your stories @creativelyowned.https://www.instagram.com/creativelyowned/Start using Wispr Flow, the crazy handy voice-to-text AI that turns speech into clear, polished writing in every app. Click here.https://ref.wisprflow.ai/kathryn-thompsonFind out how to own your unique edge, amplify who you truly are and get paid for it, take your business to cosmic proportions, and have fun doing it, grab it here!!https://www.creativelyowned.com/quizJoin The Selling the Invisible AI Lab, a curated membership for founders who want to discover how to use AI in their business.https://creativelyowned.com/ai-lab

Skilled trades employers are fighting a two-front battle right now: a widening talent gap and a growing shortage of “people skills”. Deloitte projects U.S. manufacturing alone could need roughly 3.8 million new workers between 2024 and 2033—and warns that about 1.9 million of those jobs could go unfilled if the skills and applicant gap persists. At the same time, LinkedIn's 2026 Skills on the Rise report notes that communication and other people skills are among the fastest-growing capabilities employers are seeking today.So what happens when the technical work is largely standardized, but the human side of the job is far less predictable? And how do leaders build teams that can both fix the problem and earn trust while doing it?That's the core question explored in this episode of Straight Outta Crumpton, hosted by Greg Crumpton, featuring Josh Zolin, CEO of Windy City Equipment. The episode covers Zolin's unusual path from Hollywood stunt work to running a service business, why soft skills are now a competitive advantage in the trades, and how “career-path clarity” (a real roadmap, not vague promises) can change hiring and retention outcomes.The main topics of discussion…From stuntman to CEO: Zolin explains how “performance under pressure” and precision—where an inch can mean life or death—translated directly into leading a real-world service operation and developing credibility with technicians.Soft skills aren't “extra”—they're the job: The pair unpack why customer interaction, communication, and leadership behaviors often matter as much (or more) than wrench-turning, especially as teams get younger and customers expect better experiences.The roadmap advantage: Zolin shares how showing recruits a visual, leveled career path—skills, expectations, shadowing, progression, and pay bands—became a magnet for talent because it answers the question: “Where am I headed, and how do I win here?”Josh Zolin is the CEO of Windy City Equipment, a multi-state commercial foodservice repair company and three-time Inc. 5000 honoree, where he rose from field technician to executive leadership after years working directly alongside service teams. He is also the founder of Blue Is The New White Academy, an online training platform focused on developing leadership, communication, and business skills within the skilled trades workforce. In addition, Zolin hosts the podcast Everything They Don't Tell You, where he speaks with entrepreneurs and industry leaders about business growth, leadership, and the realities of building sustainable companies.

They analyze the Phillies' 38-18 surge under Don Mattingly and the communicative leadership, highlighted by Derek Hill's spectacular catch. The conversation shifts to a controversial article about C.J. Gardner-Johnson's attitude and his strange jabs at Saquon Barkley. Finally, concerns are raised over the financial stability of the Phillies' massive television rights deal with NBC Sports Philadelphia. 02:00 - Mattingly and Thomson Impact 05:39 - Gardner-Johnson Article Breakdown 09:51 - All-Star Tickets Contest 13:00 - Pitching and Trade Rumors 18:15 - Derek Hill's Amazing Catch 24:25 - Phillies Television Deal Crisis 29:10 - Orion Kerkering's Performance 37:50 - Trade Deadline Priorities 45:02 - Gardner-Johnson's Career Path

In this episode of FP&A Unlocked, Paul Barnhurst sits down with Tim Stallkamp, Senior Managing Director at Riveron, alongside co-host Glenn Snyder. Tim shares insights on interim management and how it is reshaping finance leadership by helping companies fill critical gaps, accelerate value creation, and bring experienced finance leaders into organizations during transition periods.Tim Stallkamp is a Senior Managing Director and the leader of Riveron's Interim Management practice. He is a seasoned turnaround executive who provides interim management and turnaround management services to distressed and under-performing businesses. Tim has led turnarounds as interim chief executive officer (CEO), chief restructuring officer (CRO), chief transformation officer (CTO), and chief financial officer (CFO). Expect to Learn:Why private equity is driving demand for interim finance leadersWhat makes a strong interim FP&A and finance professionalWhy broad, cross-industry experience matters more than narrow expertiseHow interim leaders create value from day oneThe importance of clear scope and alignment in engagementsHere are a few relevant quotes from the episode:"Interim management isn't about replacing people; it's about supplementing leadership to create value faster and help teams reach their full potential." – Tim Stallkamp"The more experiences an individual has across industries and roles, the more effective they are in interim management. Breadth of expertise is key." – Tim StallkampTim explains how interim management is reshaping finance leadership by bringing experienced professionals into organizations during critical transitions. These leaders not only fill gaps but also help improve processes, guide decision-making, and leave companies stronger than before.Follow Tim:LinkedIn: https://www.linkedin.com/in/tim-stallkamp-537b0317/Company: https://riveron.com/Follow Glenn:LinkedIn: https://www.linkedin.com/in/glenntsnyder/Earn Your CPE Credit For CPE credit, please go to earmarkcpe.com, listen to the episode, download the app, answer a few questions, and earn your CPE certification. To earn education credits for the FPAC Certificate, take the quiz on earmark and contact Paul Barnhurst for further details.In Today's Episode[00:00] - Trailer[05:06] - What interim management is[07:42] - Why it's growing[09:13] - Value creation in leadership[13:12] - Defining scope clearly[17:58] - Alignment in engagements[26:11] - Types of interim roles[37:14] - Systems vs skills[42:55] - Private equity decision-making[50:42] - Final thoughts

The skilled trades need more young people, and Harrison Contracting isn't waiting for someone else to solve the problem. The company created a unique event that introduced students to painting through hands-on competition, with scholarships awarded to the top performers. In this podcast, we talk with the organizers about how the idea came to life and how contractors across the country can bring a similar program to their hometowns.

Da Eun Yoon on Names, Voice Work, and Living Between CulturesChristine interviews multilingual actor, voice artist, and audiobook narrator Da Eun Yoon, announcing her as the narrator of the middle grade novel in verse Love Language (out August 4). Da Eun shares the meaning of her Korean name and her  changing English names, describing challenges introducing “Da Eun” at Northwestern and eventually embracing it. She recounts moving to New York after graduating from Northwestern (2023), being encouraged to try voiceover, and discovering audiobook narration as a career. The conversation explores her upbringing in Korea speaking English at home, identity crises and accent work in both languages, the “shadowing” method for learning pronunciation, and feeling different across Korea and the U.S. She discusses passion as acting despite fear, her work as storyteller/translator bridging cultures, uncertainty about where to live, and how narrating Love Language resonated with her.00:00 Welcome and Guest Intro01:07 The Story Behind Her Name01:55 English Names and Identity04:06 Announcing Love Language04:37 Career Path to Audiobooks06:34 Living Between Cultures09:27 Relearning Korean and Shadowing11:32 Northwestern and Family Talk12:24 Finding Passion and Fear14:10 Where to Live Next15:18 Final Thoughts on Love Language16:14 Thanks and Goodbye

Like the show? Show your support by using our sponsors.   Need to update your shop systems and software? Try Tekmetric HERELaunch your tool game to the next level with Launch Tech USA! HEREIn this episode, Jeff Compton sits down with Kansas technician Eric Schoenberger of Holt Motor Company. Having grown up around Chrysler dealerships alongside his father, a veteran drivability and transmission specialist, Eric shares his experiences in dealer life and why he ultimately transitioned to the independent repair world. The conversation explores flat-rate frustrations, warranty and recall work, shop politics, diagnostic strategies, transmission repairs, evolving technology, and the value of ongoing training. Eric also discusses how independent shops offer less stress, and a different approach to customer service and technician growth.Timestamps: 00:00 Podcast Welcome and Holiday 00:45 Kansas Guest and Vision Talk 01:30 Shop Intro and Dealer Roots 06:03 Family Influence and Career Path 08:12 Flat Rate and Recall Frustrations 17:25 Favorite Dealer Work and Transmissions 23:03 PT Cruiser Love-Hate Jobs 25:35 Diagnostics and Techline Support 27:49 Dealer Life and Shop Politics 32:37 Misfires, Burnt Valves, and Borescopes 36:31 Pentastar Problems and Tips 41:26 Diesel Disasters 44:04 Recall Work Realities 49:21 Hybrid Battery Discussion 52:37 Leaving the Dealer World 55:47 Advisors, DVI, and Communication 01:01:38 Training Great Advisors 01:08:35 Transmission Service Debate 01:14:38 Moving to Independent Shops 01:16:27 Learning Through Service Information 01:18:46 Oddball Repairs and Old Mopars 01:27:05 Caravan Rear A/C Repairs 01:33:05 Training Events and Mentors 01:38:49 Shop Culture and Dispatching 01:40:44 A/C Diagnostics and Leak Testing 01:47:37 Parts Support Challenges 01:54:02 Technician Pay and Flat Rate 01:56:23 Gravy Work vs. Diagnostics 02:02:27 Independent Shop Mindset 02:12:28 Training and Networking 02:20:06 Final Thanks and Wrap Up Follow/Subscribe to the show on social media! TikTok - https://www.tiktok.com/@jeffcompton7YouTube - https://www.youtube.com/@TheJadedMechanicFacebook - https://www.facebook.com/profile.php?id=100091347564232

On this episode of Live From The Compound, Josh Brown sits down with Ric Edelman, founder of Edelman Financial Engines to discuss the future of higher education, why financial planning may be one of the most AI-resistant professions, and the growing advisor shortage facing the industry. With 300,000 financial advisors in the U.S. and 38% expected to retire within the next decade, he explains why he's investing in the next generation through Rowan University's new School of Financial Planning. Plus, Ric shares his latest thoughts on crypto, institutional adoption, regulation, and why he's staying bullish despite a challenging 2026 for digital assets. This episode is sponsored by Betterment Advisor Solutions. Learn more at ⁠https://betterment.com/advisors.⁠ Sign up for ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠The Compound Newsletter⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ and never miss out! Instagram: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://instagram.com/thecompoundnews⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Twitter: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://twitter.com/thecompoundnews⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ LinkedIn: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.linkedin.com/company/the-compound-media/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ TikTok: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.tiktok.com/@thecompoundnews⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Investing involves the risk of loss. This podcast is for informational purposes only and should not be or regarded as personalized investment advice or relied upon for investment decisions. Michael Batnick and Josh Brown are employees of Ritholtz Wealth Management and may maintain positions in the securities discussed in this video. All opinions expressed by them are solely their own opinion and do not reflect the opinion of Ritholtz Wealth Management. The Compound Media, Incorporated, an affiliate of ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Ritholtz Wealth Management⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, receives payment from various entities for advertisements in affiliated podcasts, blogs and emails. Inclusion of such advertisements does not constitute or imply endorsement, sponsorship or recommendation thereof, or any affiliation therewith, by the Content Creator or by Ritholtz Wealth Management or any of its employees. For additional advertisement disclaimers see here ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://ritholtzwealth.com/advertising-disclaimers⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Investments in securities involve the risk of loss. Any mention of a particular security and related performance data is not a recommendation to buy or sell that security. The information provided on this website (including any information that may be accessed through this website) is not directed at any investor or category of investors and is provided solely as general information. Obviously nothing on this channel should be considered as personalized financial advice or a solicitation to buy or sell any securities. See our disclosures here: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://ritholtzwealth.com/podcast-youtube-disclosures/⁠⁠⁠⁠⁠ Learn more about your ad choices. Visit megaphone.fm/adchoices

Miki Feldman Simon, is an executive coach, speaker, and the author of CORE Leadership: A Four-Step Framework to Lead Yourself, Grow Your Influence, and Amplify Your Impact.Miki's perspective on people, performance, and what it really takes to lead effectively across different environments was shaped by two different lenses. First, a wide range of leadership roles, including marketing, operations, and HR leadership in companies that went through successful mergers and acquisitions. Second, growing up in multiple countries and cultures: from Israel to Australia to the United States.In our conversation, we explore how our assumptions shape the way we lead, the importance of understanding what a role really looks like before committing to it, and why many of us operate on autopilot more than we realize.We also dive into her CORE leadership framework—Clarify, Operationalize, Reflect, and Evaluate—and how it helps leaders move from reactive habits to intentional action. Along the way, Miki shares powerful personal stories and practical examples from her coaching work that bring these ideas to life.Contact Dino at: dino@al4ep.comWebsites:mikifeldmansimon.comal4ep.comAdditional Guest Links:LinkedIn: linkedin.com/in/mikifeldmansimonInstagram: @mikifeldmansimonFacebook: facebook.com/miki.feldmansimonAuthentic Leadership For Everyday People / Dino CattaneoDino on LinkedIn: linkedin.com/in/dinocattaneoPodcast Instagram – @al4edp Podcast Twitter – @al4edpPodcast Facebook: facebook.com/al4edpMusicSusan Cattaneo: susancattaneo.bandcamp.com

In this episode, Chris and Mecca discuss the non-academic job search and career experience with Dr. Anneliese Long, as well as her work assessing the connections among inflammation and ovarian reserve biomarkers. Anneliese Long is an applied anthropologist with a background in studying the biological and sociocultural aspects of reproductive health and fertility. She completed her B.A. in anthropology at the University of South Florida, followed by her PhD in Biological Anthropology at the University of North Carolina at Chapel Hill in 2025. She now works in the market research industry as a quantitative data analyst at OptiBrand Rx, where she helps bridge the gaps in knowledge between biotechnology and pharmaceutical organizations and healthcare practitioners. She also continues to teach and mentor students part-time in her home department at UNC-Chapel Hill. Contact Anneliese at anneliesemlong@gmail.com, https://www.linkedin.com/in/annelieselong/ ------------------------------ Find the paper discussed in this episode: Inflammation and Ovarian Function in Reproductive-Aged Women https://onlinelibrary.wiley.com/doi/10.1002/ajhb.24196 ------------------------------ Contact the Sausage of Science Podcast and the Human Biology Association: Facebook: facebook.com/groups/humanbiologyassociation/, Website: humbio.org Chris Lynn, Co-Host, Website: cdlynn.people.ua.edu/, E-mail: cdlynn@ua.edu Mecca E. Howe, Co-Host, E-mail: howemecca@gmail.com, LinkedIn: https://www.linkedin.com/in/mecca-howe/

Have you ever wondered if Accounts Payable is actually a good career path? Can you grow beyond data entry? And how do people move from entry level position in AP to Manager — or even into senior finance leadership? Or other roles. In this video, we're breaking down the Accounts Payable Career Path from start to finish. And to be clear, not everyone starts at the same point. The truth is, Accounts Payable is no longer just about processing invoices. Modern AP professionals play a huge role in cash flow management, vendor relationships, financial operations, and business efficiency — making it one of the most underrated careers in finance today. Link to You Won't Believe How Many Things AP Teams Manage! https://youtu.be/r-68nnxPWt8 Subscribe for more tips and insights like this: https://www.youtube.com/APNow?sub_confirmation=1 Looking for more of the most current business intelligence about + Best practices around your payment and accounts payable function + Current and new fraud protection protocols + The newest technology impacting your accounting, accounts payable, and payment functions + Career advancement +And much more!! +++++++++++++++++++++++ See most recent videos at: https://www.youtube.com/@APNow/videos

Carrie is a woman who has done a lot in her career but is this new endeavour pushing it too far?Subscribe on LiSTNR: https://play.listnr.com/podcasts/carrie-and-tommySee omnystudio.com/listener for privacy information.

In this episode of Humans of Agriculture, Oli sits down with Hamish Irvine, Head of Southern States at Halter Australia, to explore the technology, leadership and mindset driving one of agriculture's fastest-growing companies.Growing up on a mixed farming operation in western New South Wales, Hamish always imagined a future on the land. But after drought forced a change in direction, his career took him through the meat industry, global supply chains and commercial leadership before landing at Halter, the virtual fencing and livestock management company now transforming grazing systems across Australia and New Zealand.This conversation goes beyond the technology itself. Hamish shares the lessons that shaped his career, what it takes to build high-performing teams, and why feedback, accountability and culture are critical in fast-growing businesses. He also unpacks how Halter is helping farmers rethink labour, pasture management and livestock performance through virtual fencing and real-time animal insights.From career progression and leadership to innovation and the future of livestock farming, this is a conversation about embracing change and creating impact.Key insights from the conversation:How growing up on a family farm and experiencing drought shaped Hamish's career journeyWhy stepping outside traditional agriculture career pathways can create unexpected opportunitiesThe lessons learned from building a career across meat processing, sales and agribusiness leadershipWhat high-performance culture looks like in practice and why feedback is central to successHow Halter uses virtual fencing and animal insights to transform livestock managementThe three biggest drivers of value for farmers: pasture utilisation, labour efficiency, and animal healthWhy adopting new technology requires trust, clear outcomes and a willingness to changeHow innovation is reshaping the future of livestock farming and creating new opportunities across agricultureThe importance of leadership, ownership and accountability in building successful teamsWhy agriculture needs more people focused on solving industry challenges rather than following traditional career pathsChapters:00:00 Introduction to Halter and Hamish Irvine02:05 Career Path and Early Experiences04:37 The Importance of Diverse Experiences08:37 Reflections on Career Choices10:37 Leadership and Team Building at Halter12:17 Transitioning to Halter: Embracing Change20:13 High-Performance Culture at Halter26:56 The Art of Feedback and Communication30:18 The Birth of Halter: A Journey from Dairy Farming to Tech Innovation34:37 Expanding Horizons: Halter's Growth in Australia37:36 Understanding Farmer Needs: The Halter Approach39:59 Building Trust: Overcoming Skepticism in Agriculture43:45 Transformational Technology: The Future of Farming46:42 Bridging Agriculture and Technology: The Ideal Candidate48:07 The Future of Agriculture: Opportunities and Challenges Running a farm business comes with its challenges; from seasonal conditions to rising costs and cash flow uncertainty, there can be many unknowns along the way. Regional Investment Corporation, simply known as RIC, is the Australian Government's agri-lending specialist, providing low interest loans to help eligible farm businesses navigate challenges. Whether that's starting out, planning for succession, or managing through tough conditions like drought and natural disasters, RIC helps viable farmers to keep farming. With concessional interest rates, RIC loans can provide valuable breathing space, helping farmers manage cash flow while they get through tough times or to build their business. Every situation is different, so it's important to understand what support may be available and what's involved before applying. Visit ric.gov.au to learn more, explore your options, and check your eligibility.

Thanks for listening, and please follow us on Insta @NHPTalent and www.youtube.com/thePOZcast For all episodes, please check out www.thePOZcast.com This special episode is brought to you by our dear friends at Blood Cancer United. An organization very near and dear to me. I'm here to remind you to give to causes that make a difference. You want to help, but you don't know where to start? Blood Cancer United is at the top of my list. They are the global leader in helping patients and families with blood cancer, and your dollars fund research, patient support, and advocacy. Please give today here: Thank you for supporting this important mission. Learn more and donate here: https://pages.lls.org/voy/nyc/nyclls26/aposner   Chapters 00:00 Introduction to Leah Sullivan and TaskRabbit 03:04 Leah's Early Life and Career Path 05:58 Transition from IBM to Entrepreneurship 08:57 The Birth of TaskRabbit 11:55 Challenges in Building a Marketplace 14:59 The Evolution of Gig Work and Future Perspectives 21:30 Building a Team and Hiring Practices 24:41 Managing Challenges as a Founder 26:34 The IKEA Acquisition: Lessons Learned 31:18 Redefining Identity After an Exit 33:32 Investing in Founders: What to Look For 34:59 Creating a New Venture Ecosystem 36:31 The Importance of Community for Founders 41:59 Defining Success: Winning vs. Impact

Marina George, Field Service Engineer at Oxford Instruments, never planned to end up in field service — she wanted to be a doctor, then fell in love with research. As the first female guest on Frontline UNSCRIPTED, she shares an unconventional path into the industry, what it's been like as a woman in a field with very few, and why "just be yourself" is the best advice she ever received.

Executive coach Julian Lighton, author of Navigating Your Next and a former McKinsey associate partner, explains how to take control of your career by clarifying what you truly want and building a deliberate path to get it through his seven-step framework. He breaks down the four unavoidable career transitions, why no meaningful goal is reached alone, and why success always comes at a cost. Learn more at https://www.julianlighton.com/.

Andy Kvesic left the job every lawyer wants to build something the profession had never seen. As CEO of Aprio Legal, he traded a general counsel role at a thriving family office for the harder, riskier work of acquiring a Phoenix law firm and redesigning how professional services actually work. The result is a historic combination: the first time two Alternative Business Structure firms have merged, bringing together a corporate law firm and a national accounting and advisory firm backed by private equity. Attorneys, accountants, wealth planners, and business advisors now serve the same clients under one roof. The idea came from watching entrepreneurs waste time and energy bouncing between disconnected professionals who never coordinated with each other. Arizona's 2021 rule change allowing non-lawyer law firm ownership gave Kvesic the opening to try something different. His merger with Aprio wasn't a calculated exit. It was the recognition that both firms were solving the same problem from opposite ends: Aprio's professionals were constantly referring clients out for legal work, and Kvesic's attorneys were constantly referring clients out for tax and accounting. Neither could fully serve their clients alone. Building the integrated platform also forced a reckoning with how differently law firms and accounting firms run their businesses. After two decades working almost exclusively with other lawyers, Kvesic found Aprio's infrastructure to be a genuine upgrade: multi-year planning, pipeline visibility, real margin analysis. For an industry that largely runs on a cash-in, cash-out model aimed at maximizing year-end partner distributions, the difference is significant. The legal profession is changing whether it wants to or not. The more interesting question Kvesic raises is whether the people inside it will have the courage to lead that change rather than resist it.   Episode Breakdown: 00:00 From General Counsel to Law Firm Owner: Andy Kvesic's Career Path 02:51 The Vision Behind Raddock's Law and the ABS Model 09:03 How Aprio Legal Became the First ABS-to-ABS Merger 14:57 Cultural Differences Between Lawyers and Accountants 24:33 How Accounting Firm Discipline Is Changing Law Firm Operations 29:35 Growth Strategy and the Integrated Legal Accounting Platform 37:52 ABS Advice and the Future of the Legal Profession   Connect with Andy Kvesic: Connect with Andy on LinkedIn   Andy Kvesic - CEO, Aprio Legal | Partner    Connect with Howard Rosenberg: Connect with Howard on LinkedIn  Howard's Company web profile   Connect with Chris Batz: Connect with Chris on LinkedIn  Follow Columbus Street on LinkedIn Columbus Street Website  MergerWatch Website Podcast production and show notes provided by HiveCast.fm  

In this episode of FP&A Unlocked, Paul Barnhurst sits down with Robert Blanding, an experienced finance and operations leader in the ag-tech sector. Robert shares insights on building high-performing FP&A teams, partnering effectively with accounting, developing financial acumen, and applying strategic finance to drive operational impact in global businesses.Robert Blanding is the Chief Financial Officer at Fall Creek, a global leader in the Ag-Tech sector. He brings extensive experience in finance and operations from 18 years at Intel and leadership roles across industrial manufacturing, technology, and ag-tech companies. Robert combines strategic financial leadership with a focus on innovation, long-term business growth, and developing high-performing teams.Expect to Learn:What great FP&A looks like and the importance of business acumenHow to develop strong partnerships between FP&A and accountingStrategies for building high-performing, empowered finance teamsNavigating systems, processes, and technology challenges in FP&AHere are a few relevant quotes from the episode:"It starts with being connected to the business, having strong acumen, and being consulted by stakeholders." – Robert Blanding"Investing in relationships outside of crisis is critical to getting support from accounting and operations." – Robert BlandingRobert shares practical insights for FP&A professionals and aspiring CFOs, emphasizing the importance of business acumen, strong partnerships with accounting, creative problem-solving, and developing high-performing teams.Follow Robert:LinkedIn: https://www.linkedin.com/in/rfb19/Company Website: https://www.fallcreeknursery.com/Earn Your CPE Credit For CPE credit, please go to earmarkcpe.com, listen to the episode, download the app, answer a few questions, and earn your CPE certification. To earn education credits for the FPAC Certificate, take the quiz on earmark and contact Paul Barnhurst for further details.In Today's Episode[00:00] – Trailer[03:55] – Defining Great FP&A[07:39] – Career Path & First CFO Role[17:49] – Fall Creek & Ag-Tech Overview[21:18] – Importance of FP&A & Accounting Partnership[32:23] – Creative Problem Solving & Process Improvement[38:59] – Challenges in Building High-Performing Teams[45:32] – Advice for Aspiring CFOs[49:11] – Top Technical Skill for FP&A Professionals[52:50] – Personal Interests & Basketball[56:12] – How to Connect with Robert

At some point, we have to stop and ask ourselves: What are we truly building for our future? Where is our career in healthcare going? I have all of these credentials in coding, billing, compliance, practice management, and prior authorization, but what does that really say about me? I want to be clear that I believe credentials matter. I know the work, time, money, and dedication it takes to earn them. However, we also need to be honest with ourselves about something that is becoming increasingly obvious in healthcare: collecting credentials alone is not a career plan. AI is changing healthcare, and we need to start looking at the bigger picture. Terry discusses what that means in today's episode of the CodeCast podcast and asks the questions that may be uncomfortable to consider. Subscribe and Listen Find all of Terry’s official links in one place: https://www.terryfletcher.net/links The post Credentials Alone Are Not a Career Path in Healthcare appeared first on Terry Fletcher Consulting, Inc..

Annie sold a multi-million-dollar therapy center at 43. On paper, that kind of success should make a person feel safe. But money does not automatically rewrite what the body learned first. Host Syama Bunten sits down with Annie Wright, a licensed psychotherapist and executive coach specializing in trauma recovery for high achievers, for a conversation about what financial success can and cannot fix. For Annie, psychological healing and financial healing have never been separate work. Her childhood financial trauma shaped more than her beliefs about money. It shaped what safety, success, and self-worth felt like. Annie grew up between old-money privilege and real financial instability, watching money appear, disappear, and come with secrecy, shame, and survival. That early relational trauma and money mindset followed her into adulthood, even as she became the first in her family to build the kind of security she once imagined from a distance. This is a conversation about breaking the poverty cycle, first generation wealth building, and the emotional cost of becoming the person no one in your family knew how to model. Annie is honest about ambition as a survival strategy, the nervous system that still braces for everything to disappear, and why the numbers on paper do not always match the feeling of safety inside. Now, her work sits at the intersection of women and financial healing, with books, courses, and education designed to help more women move from survival into lives that feel secure, self-directed, and fully lived. Her 2026 book Decade of Decisions is part of that next chapter. If Annie's story speaks to you, keep going. The Wealth Catalyst Freedom Tour is bringing intimate money conversations to women in 32 cities this year. The Wealth Catalyst Summit lands in San Francisco this October for a full day built around what comes next. Find your city at wealthcatalyst.com.   Episode Breakdown: 00:00 Meet Annie Wright: Psychotherapist, Executive Coach, and Exited Entrepreneur 02:28 Growing Up Between Poverty and Old Money on the Coast of Maine 06:28 How Childhood Financial Trauma Shapes the Way Kids Survive 07:54 Getting a Full Ride to Brown and the Drive Behind It 11:25 The Peace Corps, a Breaking Point, and the Start of Healing 15:24 Burning Through Savings and Finding a Career Path at Esalen 17:43 Graduate School Debt, Minimum Wage Internships, and Financial Fear 23:45 Budgeting From Zero and the Financial Sobriety Journey 28:23 Launching a Therapy Center on Mat Leave and Betting on Herself 30:49 Being the Primary Earner and Making the Stay-at-Home Partner Decision 34:52 Knowing When to Sell and the Exit That Changed Everything 38:22 Trauma Recovery for High Achievers and the Mission Behind the Work 41:46 What Comes Next: Books, Courses, and Scaling the Impact 47:27 How to Find Annie Wright and What She Needs From You   Connect with Annie Wright: Visit Annie's website Subscribe to Annie's Substack   Find more from Syama Bunten: Attend a Salon near you: wealthcatalyst.com/salons Instagram: https://www.instagram.com/syama.co/ Join Syama's Substack: https://thewealthcatalystwithsyama.substack.com/ Website: https://wealthcatalyst.com Download Syama's Free Resources: https://wealthcatalyst.com/resources Wealth Catalyst Summit: https://wealthcatalyst.com/summits Speaking: https://syamabunten.com Big Delta Capital: www.bigdeltacapital.com Podcast production and show notes provided by HiveCast.fm

In this episode of the RSNA RadioGraphics team podcast mini-series, Dr. Jason Cai interviews our guest, Dr. Laura Oleaga, a neuroradiologist at the Hospital Clinic Barcelona, about her insights on the rewards and challenges of an academic radiology path. She discusses the key responsibilities, necessary skills, and mindsets, as well as tips for finding the right mentors. Whether you're interested in education, research, or leadership roles, Dr. Oleaga provides valuable guidance to help trainees make an informed decision about their future career direction.

This year, China's national college entrance examination is underway. It marks a major milestone, as students begin to decide on different career paths and areas of specialization. In this episode of Takeaway Chinese, we explore the professions people pursue today — and take a look back at the imperial examination system in ancient China. On the show: Niuniu & Steve. (08:43) What were the imperial exams like in ancient China? (16:58) Education systems in China today.

Discover all of the podcasts in our network, search for specific episodes, get the Optimal Living Daily workbook, and learn more at: OLDPodcast.com. Episode 2072: Laura Gariepy explores the complex decision between choosing a career driven by passion or one focused on financial success, revealing that the smartest path is often more nuanced than a simple either-or choice. By examining multiple career strategies, from pursuing profit first to blending fulfillment with financial stability, she offers practical insights to help you align your work with both your personal values and long-term goals. Read along with the original article(s) here: https://womenwhomoney.com/passion-profit-best-career-path/ Quotes to ponder: “Determining whether to prioritize the pursuit of wealth or personal fulfillment is perhaps the most critical aspect of choosing a career path, making a professional pivot, or pursuing an entrepreneurial idea.” “You obviously need to earn enough money to cover your desired lifestyle. But, selecting a career path exclusively for financial gain may not be a good solution for your overall wellbeing.” “Truly knowing yourself will help you make professional career choices in line with your personality and life goals.” Learn more about your ad choices. Visit megaphone.fm/adchoices

If you've been looking for a way to use your clinical skills, stay patient-facing, and make an impact—without the full weight of traditional practice—this episode may open a door you didn't know existed. Today, I'm joined by Dr. Purvi Mehra, who shares her unexpected path from fellowship into clinical research and ultimately building and selling a thriving research company. We explore what the principal investigator role really looks like, why it's far more clinical than most physicians assume, and how you can get started even without prior research experience. If you're looking for a flexible path that allows you to stay patient-facing while shifting out of traditional practice, this conversation opens a fascinating and often overlooked opportunity. In this episode we're talking about: What a principal investigator actually does day to day in clinical research Why private clinical research is more patient-facing than you might think How Dr. Mehra transitioned from fellowship to building and selling a research company The different types of clinical trial settings and what to expect in each Who this role is a good fit for and the skills you already have that apply How to find opportunities even if you have no research background Compensation insights and what physicians can expect in these roles Links for this episode: Dr. Purvi Mehra's Website Dr. Purvi Mehra's LinkedIn Exit With Intention - A book for healthcare business owners considering an exit by Dr. Purvi Mehra If you would like some confidential help with your career situation, I offer an hour-long paid consultation via Zoom. This session may be all that you need to gain clarity and have some steps for moving forward. If after this consultation you prefer additional support, there is the option of doing one of my coaching programs (subject to availability). For more information including pricing please reach out to Kati at team@doctorscrossing.com. Thank you for listening!

We're announcing AIEWF speakers this week! Take the AI Engineering Survey!Today's guest Ethan first joined us for the LS Paper Club as the lead on NVIDIA Cosmos World Model, but then joined xAI and built Grok Imagine in 3 months:He comes back on Latent Space with some nuclear hot takes: that Video Models primarily get their intelligence from LLMs, not from training on video data, and that the next frontier for truly interactive, realtime, long-horizon world models is to work on LLMs (perhaps Interaction Models as well…)Put it this way: In the near term, the next Sora won't be a better video model, but a video agent.Generative Media may more closely follow the evolution of AI coding which went from focusing on one-shot output performance and cost, to multiturn reasoning and planning models for agents and systems that can plan, edit, test, debug, and submit PRs.At a certain point, coding models got so good that the only significant next step to improve performance was handling the orchestration of these models.Now as the performance of video models increases significantly across realism, consistency, & prompt adherence while becoming more cost efficient, the next evolution of video generation may also be systems that can plan, generate, edit, critique, and iterate across an entire creative task. In this episode, Ethan joins swyx and Vibhu to unpack what it actually takes to build frontier image and video systems: data, VAEs, diffusion transformers, audio-video alignment, inference speedups, and the hidden cost of storing and moving massive video datasets. From building NVIDIA's Cosmos world model to joining xAI as Grok Imagine was being built from zero to one, Ethan He has been at the center of some of the most important work in video generation, multimodal models, and real-time world models.We go deep on Grok Imagine, how a small xAI team shipped its first multimodal video model in three months, why iteration speed matters more than almost anything in model development, and why many of the biggest gains come from fixing tiny bugs in data and training pipelines. Flipbook: The future of VideomaxxingVideo agents are almost a sure bet to be the trend in the coming year. We end with a glance at what's beyond video agents:Flipbook caused a minor sensation this year when it was released, but most treat it as a fun demo. Ethan takes it very seriously — with the speed and cost of inference coming down every year, the future of custom video JIT UI is closer than you think. We talked about why videogen models may become the front end of AI, how generative UI could replace traditional HTML/CSS, why world models need to be real-time, interactive, and long-horizon, and why the future of video generation may depend more on language models and agents than on diffusion alone.We discuss:* Why fast iteration mattered more than meetings* Why small training bugs can drive huge model quality gains* Why coding models may make compute the bottleneck again* How image and video models are trained with synthetic captions* The role of VAEs and latent space in frontier video models* Why image models are the foundation for video models* The tradeoff between temporal compression and real-time interactivity* Flipbook, Neural OS, and the future of generative UI* Why future interfaces may go from user intent to pixels* The hidden cost of training video models: storage, egress, and GPU hours* How step distillation and consistency models (like OpenAI sCM) makes video inference orders of magnitude faster* Grok Imagine 0.9 and large-scale audio-video generation* Why audio-video alignment is harder than text-video alignment* Ethan's definition of world models* Reference-to-video, video extension, and long-context video generation* Why xAI's research communication undersells Grok Imagine* How xAI culture shaped the speed of development* AI watermarking, SynthID, and detecting generated media* Why prompt rewriting matters for video models* Grok Imagine Agent and the rise of video agents* Why language models may unlock better video generation* Robotics, physical AI, and embodied world models* Why Ethan left xAI and shifted focus toward LLMs* Self-managed context, memory, and the next frontier for language modelsEthan He* LinkedIn: https://www.linkedin.com/in/ethanhe42* X: https://x.com/EthanHe_42Timestamps00:00:00 Introduction00:01:25 From NVIDIA Cosmos to xAI00:03:24 Building Grok Imagine from Zero to One00:10:07 How Image and Video Models Are Trained00:18:53 Video Compression, VAEs, and Real-Time Tradeoffs00:22:10 Generative UI, Flipbook, and Neural OS00:32:10 The Cost of Training Large Video Models00:37:04 Distillation, GANs, and Fast Video Inference00:41:21 Audio-Video Generation and Grok Imagine 0.900:48:34 What Makes a World Model?00:55:51 Reference Videos, Long Context, and Video Memory01:00:11 xAI Culture, Research, and First-Principles Building01:09:45 AI Safety, Watermarking, and Prompt Rewriting01:13:10 Video Agents and AI-Assisted Creation01:27:32 Why Language Models Unlock Better Video01:31:15 Robotics, Physical AI, and Embodied World Models01:32:38 Why Ethan Left xAI01:34:16 Self-Managed Context and the Future of LLMs01:38:43 Ethan's Career Path and Closing ThoughtsTranscriptIntroduction: Ethan He, Latent Space, and the Path to xAISwyx [00:00:00]: We're here in the studio with Ethan He, most recently of xAI. Welcome.Ethan [00:00:10]: Thank you. Glad being here.Swyx [00:00:11]: We're also here with Vibhu. you were first coming to us or joining the latent space world because you were working on Kosmos at NVIDIA, and you did a paper. We loved it. you presented it as well, so thank you for doing that.Ethan [00:00:23]: I've actually, I also presented the MoEs twice at latent space.Swyx [00:00:29]: How did you actually hear about us? Did we reach out to you? Is that how it worked?Ethan [00:00:33]: No, actually, I-- the community. Like I realized, oh, there is this online community that people talk about AI and also learn from each other through papers every week through the Paperclip. It's very nice.Ethan [00:00:49]: I learned a lot.Swyx [00:00:49]: I think three years stop. We haven't stopped even on Christmas and New Years. many weeks I want to stop but it keeps going.Vibhu [00:00:58]: No, that was good. I think you had posted that you worked on a paper, and I was “Oh, very cool. We have Paperclip. Present then.”Vibhu [00:01:04]: But I might have reached out to you after.Swyx [00:01:05]: you-- because it's an amateur club, right?Swyx [00:01:08]: so it's very unusual and but we have sometimes paper authors come by and actually explain the paper. Today we just did, the poolside paper, which was apparently very good.Vibhu [00:01:18]: Came out yesterday.Vibhu [00:01:19]: pretty interesting, right? Fully open. They talk about everything, systems. So it's a good one. We'll, we'll recommend people to read it.Swyx [00:01:25]: Bring us up to speed on your transition to xAI, ‘cause I actually don't even know when you joined. just like tell the, tell the story about the sort of transition.From NVIDIA Cosmos to xAI: Scaling Video and World ModelsEthan [00:01:34]: Before xAI, I was working on Kosmos world model as in-- at NVIDIA. So Kosmos is, it's a giant video foundation models that can-- that aims to simulate the world and for-- it serves as a foundation of-- for all of the roboticists to build on top of. There, once I built the Kosmos one, I realized as this thing also has a scaling law similar to language model, we need to scale up the video models further. that's, that's why I realized I need to move to somewhere with much more compute resources. That's how ISwyx [00:02:13]: Than NVIDIA?Vibhu [00:02:14]: The GPU rich came themselves.Vibhu [00:02:19]: And timeline-wise, when was Kosmo? It was pretty early, right? It was open world model, open paper, everything.Ethan [00:02:25]: It was end of twenty-four.Vibhu [00:02:28]: End of twenty-four.Ethan [00:02:30]: Then at mid twenty-five, I moved to xAI. At that time-- I joined about the time when xAI was about to build video models and in multi-model models. There were no infra, no data, and no model, and it just-- as a few engineers, we built it in three months and released the first model, Grok Imagine zero point nine.Ethan [00:02:55]: And since then, I keep working on video models and move more from training and to post-training of the video models. For example, like a reference to videos, kind of like the cameo feature and, video extensions. And, before I left, I worked on a world model, leading a small team to focus on the real-time long horizon video generation.Building Grok Imagine From Scratch in Three MonthsSwyx [00:03:24]: Can you give like a rough roadmap of okay, you're on a brand-new team. Grok previously was only text, or they partnered with BFL for their image gen stuff. What do you-- what are the building blocks, right? You have compute, data you can procure somewhere. Like just what are like the sequence of things that people should think about when you're setting up a new team?Vibhu [00:03:43]: actually even deeper, not just data you can procure. You guys had to go through getting the data too, right? So you shipped it pretty fast, but yeahSwyx [00:03:51]: three months is likeVibhu [00:03:52]: From everythingSwyx [00:03:52]: actually like very surprisingly fast.Ethan [00:03:55]: One thing I say like thanks to my experience at NVIDIA, ‘cause first time when we were building Kosmos together, we built it, for about a year. So this is like the second time I do it. Roughly have an idea, what to do. I say the most important thing is the talent. Everyone were very strong and clever, very close with each other towards a common goal. So that speed up things a lot. So you reduce the communication bandwidth among people, and everyone can work towards the same goal. It's, it's like every day there's not that much meetings on the calendar, like maybe like a, like a sync a day, and after that it's, it's just all building. It was pretty fun at that time.Ethan [00:04:47]: And another thing is that xAI has very strong foundations of like data inference, model inference, and the supporting there can help the model develop a lot. When I look at, training models, I don't so actually the top important thing is like how many, how many iterations can you do, per day? and the more iteration can you do, you can, you can train the model much faster. So if you have very strong infra and you have a lot of compute, you can, you can train these models in very short period of time. That can give you a much larger buffer to, for errors, and it also gives you the opportunity to spot more bugs.Iteration Speed, Compute, and Debugging Model PipelinesSwyx [00:05:46]: What is an iteration? Is it like a few hundred steps or what are youEthan [00:05:50]: Let's say just the train-training the model, like from acquire new data and maybe design new algorithms and train a new model, maybe at smaller scale orSwyx [00:06:01]: So cycle time for like any hyperparam that you're searching.Ethan [00:06:04]: Cycle time and tune to like eval this model. Is this model better than my previous iteration?Ethan [00:06:11]: SoSwyx [00:06:11]: So it's like before you, someone had already set this up that you can iterate very quickly.Ethan [00:06:15]: I think the foundation there is extremely good forDeveloping and research models.Ethan [00:06:23]: And often I find is it-- this is kind of boring, but like a lot of the improvements does not come from new algorithms. It comes from finding small bugs here and there in the data pipeline, in the, in the model training pipeline. Those give, those give the biggest boost to the model quality.Vibhu [00:06:46]: It's interesting, right? So you say it's like small team, less communication bandwidth, but also a lot of quality is like find little bugs. It seems counterintuitive, right? You have a lot of people, you can iron out more of those, but it's interesting to see the other side, right?Swyx [00:07:00]: I also wonder, have you-- do you try using LLMs to look for bugs? I don't know.Ethan [00:07:05]: I remember at that time it was mid two thousand and twenty-five, so it's the coding model wasn't quite there yet. I remem- I remember like December two thousand and twenty-five, it was extremely good. Yeah, I've been, I've been using it at that time. It's, it's helpful. sometimes it produce codes that are kind of difficult to maintain, even though like the first time it built something extremely fast. But it gave the, like a spaghetti code, thousands of lines that I couldn't maintain, and the LLM itself couldn't figure out what's, what's wrong and how to improve on top of it. But now I find it much better. Yeah, I want to bring up another point here is now coding models are much more efficient and can help us implement stuff much faster. Compute might become a bottleneck again because previously, like if you want to train a new model, say you want to generate new synthetic data and then or write a new algorithm, it might take a few weeks. And during that period of time, you don't-- you might not have experiments to run. But now you can build that thing within a few hours, then you can immediately train a model.Ethan [00:08:24]: Now you have to have enough compute to try all of the ideas. So compute might be the bottleneck of iterating speed again.Swyx [00:08:36]: yeah, I actually, honestly, I think it's like kind of a stressful job because you're “Well, I should be trying everything, and if I'm not, then I'm not doing my job well.”Vibhu [00:08:48]: there's also the stress of you're eating thousands of GPUs per hour, which is very expensive and, compute can go to other researchers.Swyx [00:08:56]: You got the daddy Elon toVibhu [00:08:57]: You got daddy Elon.Ethan [00:08:59]: It wasVibhu [00:09:00]: But there's still finite amount of compute, like you want to use it, you want to use it well, you want more of it.Ethan [00:09:06]: That was quite stressful indeed. Yeah, I think one thing is the-- with coding models now, like a lot of these jobs can be automated, which is much better. A second, it's a, it's a marathon, so you got to maintain good health and, a regular schedule.Vibhu [00:09:28]: It's, it's hard to hear that when you shift from zero to nothing in two months.Swyx [00:09:32]: and, I think obviously the culture at xAI is very famously, people work very hard. one thing I did want to dive into, in our-- in the notes that you, that you sent ahead of time, you had specific comments about the cost of Video Gen training. presumably this is on the Colossus-1, right? the two hundred megawatt cluster. Any whatever you want to just share on that.Vibhu [00:09:54]: I think there's, there's three things we're talking about, right? So there's Video Gen, there's also the Image Gen model that you put out. Do you want to like complete the, okay, so zero to one, you have a few months. Just what are the stages of create Image Gen model?Swyx [00:10:06]: Oh, yeah, maybe I got distracted.How Image and Video Models Are Trained: Synthetic Captions, Tokenizers, and VAEsVibhu [00:10:07]: Sorry. and then, from there's Video Gen, there's Audio Gen. Would love to get into those next. But what is that first few months like? So small team, a lot of bugs, iterations, but what does it look like? Do we take something off the shelf? Do we just get data compute? What's, what's the few months like? How do you go to state-art Image Gen model? How do you just start?Ethan [00:10:28]: I cannot comment specifically how xAI did, but it's, it's a quite standard process. I can draw some, examples from Cosmos. So mainly it's building a video model, you actually need to build a image model first. And building these two models, the data you need is a hundred percent synthetic pair of language and image or language to video. Because on the, on the internet, actually, the videos don't naturally associate with text. So you can say, oh, like on YouTube, you have the title and you have the description and the commentsSwyx [00:11:11]: TitleEthan [00:11:11]: of a video, but usually they're not relevant to the video itself. And say maybe like the video is a natural scene of mountains or something, and the title is, I'm so happy today.Ethan [00:11:26]: So they have they have no correlation at all. So the first step is to, you have to generate synthetic pair of language with the videos. So you gather videos from the internet, and you use a VLM to caption the videos. So that part, here's a question, like how do you, how do you gather VLM to begin with? So if there's noSwyx [00:11:55]: You, so you fuse the model, right? LikeEthan [00:11:57]: Say if there's no like VLM exists, like how do you generate the text to the beginning, right? It's, it's impossible.Swyx [00:12:04]: I see.Ethan [00:12:05]: In the beginning, it's like you ask human to describe the video as detailed as possible.For example, you ask them to describe everything, like all objects, all characters, and all interaction and dialogues in the, in the videos. So that's in the protocol of Cosmos labeling. We require the objective we give to the labelers was that you have to describe the video as detailed as possible, such that a blind person hears a blob of text can reconstruct what the video is like from their head.Swyx [00:12:43]: Video or image? You're talking about images.Ethan [00:12:44]: Video or image, either one of them.Vibhu [00:12:47]: This was pretty common when we went from clip and DALL-E, right?Vibhu [00:12:51]: It's all training on really detailed captioning of images. So same is applied to video, but insteadEthan [00:12:57]: same appliedVibhu [00:12:57]: of using multimodal model to pass in video images and write rich descriptions, you can alsoSwyx [00:13:04]: I think there's this traditional perspective of supervised, or, very highly human curated thing. I feel like there's a unlock with unsupervised, right? Where like you have enough to bootstrap that you can just throw common corpus on it or, whatever. like unsupervised vision and language pairing, right? Like where you just have, interspersed image and text and it just learns. To me, that is the VLM breakthrough that is different from the clip, different from the LM era.Ethan [00:13:36]: It's interesting to see that you kind of need both data.Ethan [00:13:41]: For example, for theSwyx [00:13:41]: You need it to bootstrap it up. YeahEthan [00:13:43]: for the generative model training, there's also usually like a small percentage of unlabeled data. So the model is instructed to generate a video without any text instruction. That can also help the model generalize. So after this stage of generative synthetic pair, so, one important common step is to train a compressor or a tokenizer of the image or videos. So because, if you train-- If you can technically, theoretically train image or video models on pure pixels, but the problem is that the, it's, it's a lot of tokens. So like one image, it's, a thousand by a thousand, it's like one million tokens, one million pixels. It's impossible to train transformer on that. So it's, you need to train a tokenizer, which can go from image to latent space and latent space back to image.Swyx [00:14:45]: That's why we named the podcast.Swyx [00:14:48]: But, basically, you're talking about vocabulary science.Ethan [00:14:50]: so vocab.Swyx [00:14:51]: And so, what is, what is imp-- like a million is impossible?Ethan [00:14:54]: In generative models, the vocab is continuous. It's a continuous space. We can think about like you map an image to a vector. It's a, it's a fixed length vector. It's sixteen or forty-eight, something like that. And then you map that vector back to the image space. And the mapping is, has-- The mapping is patch-based. So you say you haveEthan [00:15:22]: a sixteen by sixteen patch and you match, you map that patch of pixels into this latent space.Swyx [00:15:29]: We've covered thisVibhu [00:15:30]: This is like the vision transformersSwyx [00:15:32]: VAEs,Ethan [00:15:33]: VAEs.Vibhu [00:15:34]: You basically compress your input, you do your generation, you're reasoning all that generation in smaller dimension, and then you project back out.Swyx [00:15:43]: VAE is a form compression, but I think the for me, the patching thing is from VIT, right?Ethan [00:15:48]: You can make those.Swyx [00:15:49]: Literally the, yeah, the paper is titled like sixteen by sixteen is all you need. something like that. and then I think also, people make a lot of comparisons with this kind of patching with convolutions.Swyx [00:16:02]: Which is you're, you're kind of re- reconstructing the old paradigm with the new.Ethan [00:16:05]: Actually, in VAEs, there are, there are both convolution networks and transformers. You can actually do both.Ethan [00:16:14]: After this VAE, so what you've got is you've got latent space tokens and you've got the language tokens. So now the training of the diffusion transformer, usually generative models use diffusion transformers. It is actually quite standard. It's, it's very similar to how you train a language transformer models. It's not that much difference. It's just the tokens, the visual tokens in, visual tokens out. The only difference is there's a denoising process. So you train the model to unmask some of the noise. So you add, you add random noise to the visual tokens, and then you train the model to remove those noise to generate the clean tokens. Any inference, the model can iteratively remove noise from a hundred percent noise.Swyx [00:17:12]: And then there's also, to speed things along on the tech tree of diffusion, there's CFG, and then there's, there's also, latent diffusion that, there's, there's someone in there. I think, somewhere along the line, obviously, like stability and all these other guys, pioneered a lot of this, architecture. I don't know if you want to get into that or just, or do the video side up to you.Bootstrapping Video from Image Models and Temporal CompressionEthan [00:17:37]: After you train such model, such image model, the reason it's a, it's a foundation for video models is that image models are cheaper to train, and they have much denser connection between language and text. So, sorry, language and images. For example, you train a billion, you train on a billion images, and there's a mapping from the text to the image. And the cost to train the same, like the, a billion, a billion text to a billion videos, that's much more expensive because videosNaturally have more tokens than images. Because the diffusion models, their understanding of, language purely come from this mapping. So if you don't have enough mapping, so if you only train on like a ten million videos or something, there-- you might not see enough language tokens in your training, so your model does not understand human intention enough. So that's why you really-- you train-- you first train this image diffusion models, and then you bootstrap the video model from there.Swyx [00:18:53]: One thing I did want to ask, because I-- actually, I think you're, you're the first per-- video model person I've ever talked to, I think. we've, we've like talked to Luma and all those folks. There's all these tricks in video compression where basically frame by frame there's not that much difference, so actually you don't have to regenerate or save the whole frame, right? but I think MP4 compression or something else like that.Swyx [00:19:16]: is it tempting to use that? Or as far as I can tell, everyone just treats it as, “No, we would just generate every frame.” Is that roughly the state-art?Ethan [00:19:27]: There are a few different approaches. Let's say first, like you want to just directly use MP4 compression and use that as the tokens for the transformers to train, right? So people actually have tried that, but the main challenge is the latent space for the MP4 tokens were not, were not very comprehensible for the models. It's, it's extremely hard to train on that. And there's aEthan [00:20:01]: So that's why they created VAEs, which creates more continuous, latent space, so the models can understand that latent space and learn from it much easier. Even within the VAEs, there are different difficulties of the latent space. So you can imagine something the simplest, the most naive VAE is like you have an image, and you just shuffle all of the images into a, into a vector. So you don't need to train any VAEs, right? But that latent space is extremely hard for models to train on top of. That's why there are some debate on like how do you compress the tokens. So you mentioned like you can compress frame by frame. Also, you can compress, the temporal dimension.Ethan [00:20:52]: The difference is if you compress the temporal dimension, you get a much higher compression rate. Because there's temporal redundancy between frames, because, this frame and the last frame, likely they are mostly similar, so there's only some small difference. for example, I think in 12.1 VAE, they have like a eight by eight by four compression rate. So the four temporal tokens are compressed into one tokens. That can save a lot of, save a lot of the context length. If you do it frame by frame, you have to do maybe like eight by eight by one. Your context length will be four times larger. That being said, the benefit of the frame-- per frame compression, we might come back to this later, is, real-timeness and interactivity. ‘Cause if you, if you strain the output of the model, frame by frame, you can-- the model can respond to any user request immediately. So if you have like a temporal four compression, four times compression, thenSwyx [00:22:06]: It might be laggyEthan [00:22:07]: there's a lag there in nature.Swyx [00:22:10]: So you're very pilled on this. let's just go ahead and bring it up ‘cause we have the visual prepared anyway. There's some frontier applications of real-time video gen. So Flipbook is one of the examples that went viral recently, right? What is Flipbook?Real-Time Generative UI: Flipbook, Neural OS, and Diffusion Front EndsEthan [00:22:23]: Flipbook is kind of like a web brow- web browser. You can see like it has the web bro- browser UI on top. The difference is all of the UIs are generated by generative image model in real time, and anything here are fake. But you can, you can explore inside this wor- this imaginary world. Say like we-- here we have engineering the Great Pyramid. Like the model generates this for us to understand how it works, and if we want to navigate around and understand further, we can click on some of the, some of the description here, and the model will generate a new page, new subpage describing the details we want to know about.Swyx [00:23:14]: So it's basically kind of we're playing a video, but it's pausing for our next interaction, and then it just plays the next thing based on our interaction.Swyx [00:23:23]: Which is kind of cool.Vibhu [00:23:25]: and you kind of decide your story. So this was, how do you make a pyramid? levering technique seemed interesting, right? It shows how do you take Okay, I want to know what is thisSwyx [00:23:35]: The demo, the demo tweet had more animation between frames.Vibhu [00:23:38]: I think it's just skipping,Swyx [00:23:39]: Oh, it's just skipping a lot of frames.Ethan [00:23:40]: they also have a video modeVibhu [00:23:42]: It takes a lot. There's a lot of peopleEthan [00:23:42]: but, a lot of people are using it.Ethan [00:23:45]: So it's not available.Vibhu [00:23:46]: There's a live video stream. We can try,Swyx [00:23:50]: So this is an example of the kind of future that you see at the extreme. We don't-- we're obviously not in it today.Swyx [00:23:56]: But in a world where inference is completely free this is better than generating code and text?Ethan [00:24:02]: So this is, this is a final state of where Viva will be at for word model, I think. Imagine internet doesn't exist, and then you type in google.com. Like what should, what should, what should a model show you?the model can imagine something, and this is what the model imagine. And these web pages, they completely do not exist. So I think as the inference costs come down, we are going to have generative UI for everything. If you think about how the coding model works, so they write code for a web page, and they render the code might be con- converted into binary, and the binary render the pixels on the screen. So we in machine learning, every time we have some breakthrough, obviously it's, it's more intuit. So why don't we have like user instruction to the pixel directly? So the generative UI will be user intention to the pixels directly. And say like even if I want email, let's say everyone have the same interface, but I want, I want it slightly different. I want the email to show to me like a TikTok, so I can swipe left and right for the emails. And or maybe you want something else. We can have completely different things. Or like I have I'm looking at, Instagram stories, and I don't like the Like button. I always may click it. And, generative UI resolved it. So it's going to be a revolutionary replacement of the interface. So in the future, we might have much more powerfulEthan [00:25:50]: LLMs and coding models running behind the scene. And in the, in the front-end, the diffusion model will actually be the front-end to show stuff to you. That's how I imagine it.Swyx [00:26:02]: Diffusion front-end, deterministic back-end.Swyx [00:26:04]: Something like that. I find that very expensive, but,Vibhu [00:26:08]: I find it interesting you called LLMs writing code on the back end deterministic, but okay.Swyx [00:26:14]: you write it onceVibhu [00:26:15]: Compare it toSwyx [00:26:16]: And then you execute.Ethan [00:26:17]: If you think about the cost, say, let's say H100 costs $1 per hour, and if you use this eight hours a day and thirty days, so, every month you're paying this two forty, you'll actually not wanna pay for that. That's even more expensive than Cloud Code Max. But if you think about the compute costs come down like two times every year, and I think the future will likely arrive like within few years.Vibhu [00:26:49]: It's everything, right? compute cost comes down, compute gets faster, model gets smarterEthan [00:26:54]: More efficientVibhu [00:26:54]: model gets smaller.Swyx [00:26:55]: I don't know why you say two times, ‘cause I think it's like 100 times. In language models, it is roughly one hundred to a thousand times every twelve to eighteen months, for the same given level of LMSys, ELO.Vibhu [00:27:08]: That's a net of everything, right? That's model performance alongside compute. So different than just compute costs come down. But, a very interesting future.Swyx [00:27:19]: So the web designers will have to shout out that accessibility is an issue, right? how do you deal with screen readers or whatever. But yes, this is higher bandwidth storytelling than anything you can possibly generate with code, right? So I think that's the rough idea.Ethan [00:27:34]: And I'd like to add a little bit that so human naturally have the maximum bandwidth when we are looking at things, look at videos, and we also have maximum output bandwidth when we are talking. So in the future, it might be something like we talk to AI models, and the AI model responds back with a generative UI. So that would be the maximum input and output bandwidth to interact with AI models before neural link happens.Vibhu [00:28:06]: And it's also very custom, right? Some people are very visual, some people are not as visual, right? They prefer the text. But the best thing about generative UI, right, it can also be text.Swyx [00:28:17]: There's another project that we wanted to highlight, which is the Neural OS. Kinda similar idea, but here you're literally operating, simulating an operating system with a video model.Swyx [00:28:27]: and you can play Doom, you can do Firefox. I find this like mildly less impressive, obviously, because it's an OS that I can run.Swyx [00:28:37]: But here everything is imagined.Vibhu [00:28:40]: I was, used to the Command+W to close the Firefox tab. It didn't crash. That's why I saidSwyx [00:28:45]: It's too immersive.Vibhu [00:28:46]: It's, it's too immersive for me.Swyx [00:28:47]: Too immersive.Vibhu [00:28:48]: I wanted to close the tab.Vibhu [00:28:49]: But yes, I can play generated diffusion.Swyx [00:28:51]: this is shockingly fast.Swyx [00:28:54]: Because I remember there was a demo about like maybe one to two years ago. Someone tried to do the first-person shooter with a image model. There was no consistency. It was very slow. But here it looks like realistically it's-- this is Doom.Vibhu [00:29:07]: I think there's two sides to that, right? There's okay, what is running a game? The heavy part of it is actually the game engine, all the lighting, all that stuff, the graphics. This is just kind of video, right? Like we've solved consistency. This is still, it looks like a few years old image generation. There's some temporal consistency, but it's, it's kind of just images stitched together as frame video. But it's a good visual representation to pi- to picture the future you wanna see, right? that's, that's what I see in these more so.Ethan [00:29:38]: This reminds me of how the video models gets better and better. So Neural OS is kinda if you just look at it feels like it's just a crappy version of the, like the Windows we could have, right? And, but the difference is, so the model, this model is overfitted on the existing operating systems. It can generate nothing different than that. But it's actually also similar to video models. So when we are training these video model, image model, we train them on internet. There's no imaginary supernatural stuff on the internet. But once we train this model, you can prompt the model to generate something supernatural that have never existed in the data set. So if you train your Neural OS or neural computer on the standard screen recordings on the entire internet. The model can imagine completely new interface to interact with the computer.Swyx [00:30:43]: This is one of those things that is magical to me. usually generalizing out of distribution is bad, but somehow we have learned some kind of internal world model that you say, this plus, but it looks like rainbows and butterflies, it'll do it and it will kind of make sense.Swyx [00:31:03]: So yeah, that's kind of cool. Yeah, I don't know if there's any comment more on there. I do, I do wanted to, I did wanted to touch a little bit more on the model architecture stuff, which I think you were getting. It's, really fascinating. We don't get a chance to talk about this enough. So one of the papers that we covered, we've covered every annual, segment anything release. and I don't know if you follow-- you're a computer vision guy, so youEthan [00:31:26]: I knowSwyx [00:31:27]: . So they did memory attention, which is kind of interesting. And I always think, anything where you can, across the temporal dimension, keep some consistency, I think it's, very fascinating, and I don't know if Basically, does that-- the CV side bleeding into video gen side, I think is underexplored, right? we talk about it for labeling, but actually you can borrow the architecture itself.Ethan [00:31:50]: There's, there's also complete different approaches, right? you brought up the term world model, so we went from video model to world model. There is diffusion, but there's also other approaches that people are doing. So maybe we get into those after as well,?Swyx [00:32:03]: He has a whole definition of world models and stuff. I feel like we threw a lot at you. Whatever you want to comment on.Why Video Models Are Expensive: Storage, I/O, and Training ScaleEthan [00:32:10]: I think one thing that we should actually comment back on is okay, so we were talking about the steps to train image gen to video model. One thing we don't see as much of is okay, you brought up the delta in training data, right? SoEthan [00:32:24]: you won't have as much a video model might not generalize, but what is the cost of training a large video model? So we know for LLMs roughly, okay, even like the poolside thing that came out today, right? It's a Gemma level model trained on roughly forty trillion tokens at this many H200s over this much time, right? You can see what is the exact cost of that. So how many GPU hours over how much H200 costs? So how do we do the back-end math of, same thing for video models, image models. How do you, how do you kind of break that down? I can share some back-envelope calculation. So surprisingly, video models is-- the cost is very-- is comparable to language models and obviously the largest scale is language model, maybe like a medium scale to language models. I said just storing the videos alone, it costs a lot. You can, you can maybe look up on AWS or something.Ethan [00:33:20]: You really, say if you have a billion videos and let's say, let's just say like each video, like five megabyte, then you need five petabyte to just store those videos. And also remember we talk about you use a VAE to compress the videos, and you also need to store, typically you need to store those continuous feature, in-- also in your storage. That's also comparable size with the videos themselves. So just storing these videos and the features is tens of petabytes alone. And,Swyx [00:33:58]: I just, I just looked up the calculation. Five petabytes on S3 Standard is one hundred K per month.Ethan [00:34:05]: AndSwyx [00:34:05]: It's comparableEthan [00:34:05]: and you needSwyx [00:34:06]: AndEthan [00:34:06]: And then like tens of petabytes, two hundred K. And even more expensive is you have the ingress and egress.Swyx [00:34:13]: Oh, yeah.Ethan [00:34:14]: Like you-- through the internet. You have to just to download those videos, I believe it's, it's more expensive on AWS than just storing those videos.Swyx [00:34:25]: Storing, yeah.Ethan [00:34:25]: And each training runs, you probably need to pull them once. If you train multiple times, it's, it's even more than that. So it's like just storing the network, those costs is just, it would be a few, a few millions per month to just storing everything, not to mention the GPU cost.Ethan [00:34:45]: AndSwyx [00:34:45]: my side tangent, the compute rental, like GPU rental is very efficient. There's one side, okay, you can be XAI and build your data center. Should we not just build our, storage compute as well? LikeEthan [00:34:57]: Of courseSwyx [00:34:57]: cloud cost compared to just,Ethan [00:34:59]: You save so muchSwyx [00:35:00]: store. Yeah, exactly.Swyx [00:35:01]: Especially with like egress and stuff. So.Ethan [00:35:04]: That's a good idea, but it also comes to-- there are some of its own challenges.Swyx [00:35:09]: Of course, of course.Ethan [00:35:10]: like people who build the GPU data centers, they might not expect this much, storage. And yeah, people build storage, typically they just build it somewhere with just CPUs.Swyx [00:35:23]: I just looked it up. Five-- AWS only charges for egress, not ingress. Tier five for five petabytes is two hundred and thirty K.Ethan [00:35:32]: Even more expensive than the storage.Swyx [00:35:34]: But storing is per month, right? You check in, then you cannot check out. so it's so cool. It's okay. So there's that side.Ethan [00:35:41]: So the TLDR, my backhand mathSwyx [00:35:42]: Data is larger than you think. Yes.Ethan [00:35:44]: my backhand math of GPU hours times GPU cost is also very much, I'm missing some storage.Swyx [00:35:49]: You're also-- you're basically like also more IO bound than normal training.Swyx [00:35:55]: Yes. ‘Cause like data loading, so caching everything, it becomes super important.Ethan [00:36:00]: So in Cosmos, we did a lot of optimizations to make it not IO bound. So, speaking of the training, actually training the model, the GPU cost, if you look up like the open source model, how big these video models are, I think like LTX has nineteen B parameters. That's a dense model. And people are also exploring, MoEs, so it might be twenty B active and, like a hun- hundreds B, total. So that's, that's even-- that's similar size as medium-sized LLM models. And if you, if you look at number of tokens-Uh, we disclose that in Cosmos. It's also like tens of trillions of tokens on the visual tokens. So putting this together, the cost of, training these video models, it's actually comparable with LLMs. Not to mention, the infra is slightly different from LLM, so it might be less efficient to train these models.Inference Speedups: Step Distillation, Consistency Models, and GANsSwyx [00:37:04]: Do you get the benefits of traditional diffusion speed-up? So for, images, there's LCM, LoRAs for, fine-tuning. There's, there's a lot of stuff that's beenEthan [00:37:15]: Flow matching.Swyx [00:37:16]: there's flow matching. There's a lot of stuff that's been done. there's some overlap that applies to diffusion on the inference side and stuff or?Ethan [00:37:23]: so the difference-- the inference side is a completely different story.Ethan [00:37:28]: I think for the training side, it might be a little bit hard to reduce that cost. And for the inference side, the biggest gain is from the distillation of these models. You can-- It's called step distillation, slightly different from knowledge distillation in LLMs. So you-- Typically, for flow matching models, you need like 100 steps or something. Like a distortion model even need even more, like 1,000 steps to generate a good image or video. A step distillation is try to learn to generate fewer step from the model itself. It's kind of like now we-- you use the full model to generate in 100 steps, and then you take a model that only generate 10 steps and let that model to learn from the perfect one.Ethan [00:38:25]: why this workSwyx [00:38:27]: Strong to weak seemingly.Ethan [00:38:28]: It is. It's kind ofSwyx [00:38:29]: DistillationEthan [00:38:29]: kind of like strong to weak. the-- from the modeling perspective, the strong model, the teacher model is trying to model the image and videos of inter-internet, and that distribution is extremely complex. But the step distilled model is just trying to learn from the teacher. The teacher is a model, and the size is fixed, as the distribution is much simpler than the whole internet. That's the intuition I have why step distillation can work. So usually these models serve in productions, they only run in a few steps. In Cosmos, I believe we have, we have like four step and eight steps. If you do some simpler task, image-image translation, it can even run in fewer step, like one step in Cosmos Transfer.Swyx [00:39:22]: I think this is the same intuition that guides a lot of the consistency model work. I sent you a link for, SCM. I don't know if you covered that. To me, that was actually one of, the most impressive papers I've ever seen from OpenAI.Swyx [00:39:34]: That this is the unifying grand concept of consistency models. I don't know if you have any comments on this.Ethan [00:39:41]: So there are, there are a few different approaches,Swyx [00:39:46]: Oh, yeah. Here it is.Swyx [00:39:47]: Two steps versus twenty or 100 steps, whatever. It's already done.Ethan [00:39:52]: So there are, there are a few different approaches, for example, consistency model, and there are also Actually, we shouldn't forget GAN. So GAN, actually, that was, that was the OG ofSwyx [00:40:05]: OGEthan [00:40:05]: step distillation ‘cause it trained just one step to begin with. So actually, a lot of, uh-- For example, there's a distribution matching distillation which use, which uses GAN, as one of the laws for distillation. It-- GAN just tells you, “Hey, generate an image,” and thenEthan [00:40:31]: it has a discriminator to tell, is this image real or not? So the model, the model just need to learn one of the distribution, not the full distribution. Because in training, the model is asked to reconstruct the ground truth image from the internet, which is extremely hard. And in-- When you're training GAN, it's a step process. It's just a, “Hey, you generate image. Does this image look as real as the image from the internet?” Which is a much simpler task. And, yeah, combining a lot of these approaches together, people typically do that, like consistency model and distribution matching and GAN, and we can get these few step models.Audio-Video Generation and Time AlignmentSwyx [00:41:21]: Then there's one step I wanted to add, which is audio and video.Ethan [00:41:26]: So, Grok Imagine zero point nine, I believe it's, it's a first audio video transmodel deployed at a large scale. SoSwyx [00:41:39]: And that was your first model?Ethan [00:41:40]: that was, Grok Imagine's first model. It's, it's audio video, joint generation. I think the hard part is, the modality alignment, ‘cause before this transmodel, we have, we have text to video alignment. We have this, correspondence between text and video. Typically, most of the VLMs, they understand images and videos. Video's very rare, and they don't understand audio mostly. And if you look at the audio generation on the LLM side, you can talk to them perfectly fine, but if you ask them to sing a song or something, it typically is not very good. Also, they don't have, they don't have music either. The hard part is thatUh, actually audio has two component. It has like a discrete component, a continuous component. The discrete component is like the language.Ethan [00:42:44]: So when we speak, it's just, someSwyx [00:42:47]: It's an ASR issue, yeah.Ethan [00:42:49]: It's, it's text token with some characteristics, I would say.Ethan [00:42:54]: But musicSwyx [00:42:56]: I think the speech guys would disagree with this.Swyx [00:42:57]: Like disfluencies and then,Vibhu [00:43:00]: There's tones you can get angry.Ethan [00:43:01]: Well, I say largely.Ethan [00:43:03]: the mu- but the music is completely different. It's, it's very continuous, and you cannot model them like discrete tokens in language models. this is like the hard part for models is, not to mention we have to align text, video, and audio together.Ethan [00:43:26]: SoVibhu [00:43:26]: How?Ethan [00:43:28]: So significant-- some significant challenges are like-- So first, like we talk about as the VLMs, they cannot understand most of them cannot understand audio.Ethan [00:43:39]: So you have to have some way to do the synthetic data generation for audio. You have to caption the model, and that involve, that involve synthetic data and human data effort a lot. And not just surprisingly, most of the LLMs are very bad at recognizing, like the beat, tone, and the details of the of music. They can, they can give some general prediction of which song is this, but it's very hard to describe the details of the music. like we mentioned in image generation, like you have to describe image as detailed as possible so that someone blind can reconstruct that. So here is like someoneVibhu [00:44:32]: DeafEthan [00:44:32]: someone deaf can reconstruct how the music sounds like without actually listening to it. Maybe you can think of it need to have the-- or they call the script.Vibhu [00:44:49]: Subtitles, yeah.Ethan [00:44:49]: You gotta have all the details of the music, and the dialogue.Vibhu [00:44:55]: So is the challenge there typically stuff like music and audio, or is it just Like is there a baseline? Okay, there's enough data where we can understand, narration, conversation, but there's nuances in audio that's where you hit all the data issues or is it just from stage zero, you just do it all right?Ethan [00:45:15]: So one important thing is like the alignment. So the model, the model has to know like the video and audio, the, uh-- it has to have a time-based alignment, like at which time step the video and the audio token correspond to each other. But we actually don't have this kind of alignment for most of the other modalities. If you think about like text and image, text and video, they are loosely aligned. So you can, you can have a description of what's going on in the video, but you don't have to exactly, You typically don't have exact description, oh, at, time step one second like what happened?Vibhu [00:46:02]: It's veryEthan [00:46:03]: At time step two second what happenedVibhu [00:46:03]: coarse. Yeah.Swyx [00:46:05]: So what was the ideal time step? You have to oblate it, and then it's like four seconds or something.Ethan [00:46:09]: So that comes down to how you design the model to, for the model to be aware of as a time, as a time modality. So the model is like a time aware. And that's something pretty unique if you think about LLMs. So if you ask LLM to complete a task, say they, uh-- you ask them and they will say, “Oh, this task will probably take twelve hours to complete,” and they come back in one hour. Say “I've already spent two days on this and I've exhausted everything.”Ethan [00:46:47]: So the LLMs them-themselves, they don't have a sense of time there.Vibhu [00:46:53]: I actually don't think that's just them not having a sense of time. I think it's somewhat based, right?Vibhu [00:46:58]: Like you tell someone, “Okay, go work on this feature. Go implement this,” there's a general understanding you would have of how long that would take without LLMs working at LLM speed, right? So you think back like two years ago, if I tell you to like build me like a new front end for latent space, have a search bar, have all this, you'll estimate that it'll take a few days, right?Vibhu [00:47:19]: So you tell an LLM, “Go build this.” It'll take me a few days. But I think it's somewhat grounded as opposed to them not having the best-- Not saying that they have a great understanding, but I think that example is like you can see where it comes from, right? You're trained on all over the text.Swyx [00:47:35]: They're, they're trying to estimate what a human would say.Vibhu [00:47:37]: because that's what the, that's what the data kind of represents. It's not themEthan [00:47:41]: It came from the corpus on the internet. People have a estimate of how much time.Vibhu [00:47:45]: And not even just in direct like training samples, right? Just your world understanding of tokens of how long stuff takes, right? Go read a book. It'll take you a while, right?Vibhu [00:47:56]: Even if you do nothing but read a book, it takes a few days. So yeah, LLM, I read it took me a few hours.Vibhu [00:48:01]: It'll take me a few hours to go through this research. But this is a tangent.Swyx [00:48:05]: Somewhat, yeah.Swyx [00:48:06]: This is a train of thought I haven't really expressed until now is, which is basically like a full world model must also be recursive, meaning that the participant in the world model must also be aware that they have a world model. which is like this whole recursive thing down the, down the line. but yes, and that the world model can be wrong and that they need to update it and blah. Yeah. We've, argued this on the, newsletter as well, that there needs to be sort of recursive or adversarial world models.World Models: Real-Time, Long-Horizon, Interactive VideoVibhu [00:48:34]: just, to ask, how do you define world model?Swyx [00:48:38]: Oh, yeah, let's go there.Ethan [00:48:40]: SoVibhu [00:48:40]: So just for context, we talked about, video generation, and then there's a-- if you say there's a distinction between world models, what's your, what's your definition? How do you see the two?Ethan [00:48:53]: So disclaimer, I'm not going to debate, what is world model. Yeah. there are many definitions, so I'll just talk about my definition. Since I came from the multi-model, multi-model domain, so mainly talking from video. So world model is like real-time interactive long horizon videos. So there are three parts. so we-- let's talk about them one by one. So the so interaction, so we just, we just look at Facebook and neural computer. So the interaction part of it, so you, world model can allow you to interact with them through keyboard, mouse, and maybe also voice. So these all is-- all is a modality. You can, you can interact with the model, and the model should respond reasonably. Second part is real time. So once you, once, say, you move your mouse, if, say, the world model generate a game, how fast can the game respond? So if you're like professional CS: GO players- -my say, oh, you have to respond- He's beginner within sub ten milliseconds or- Yeah even less. So that's not most of the- No, sixty FPS. Let's go. Oh, three hundred FPS. Oh, five hundred FPS. Wait. okay, yeah. I didn't do the math, but yeah, okay. Uh- Yeah, three hundred FPS, that's a three millisecond. So you have to respond- Oh, s**t. Okay. YeahEthan [00:50:29]: within a millisecond. Most of the video models cannot do that. Yeah. And, but if you, say, if you have a video model that is, say, like a digital human, the response time might be more generous. Maybe typically, for real-time voice interaction, it's like two hundred millisecond. So that's, that's much more generous. But even two hundred millisecond is pretty, it is pretty tricky, ‘cause remember we mentionedEthan [00:51:01]: you have this, temporal compression coming from the VAE. So if you, if you don't compress the temporal dimension, your sequence length is going to explode. So if you want to have this real-time, real-timeness in your model, you have to do is one context problem. And the third part is long horizon, ‘cause we-- if you're not going to just play with, video games just, a few seconds, most video models only a few seconds. We're going to play with minutes, hours. The model have to be able to generate long-form content.Ethan [00:51:42]: So putting these three together, it's, real-time, long horizon interactive videos. I think the final state will be, for example, like a video, a video version of Playbook, where you can, you can interact with, a neural computer. You move your mouse, and you click on the generative interface, and it will reply to you through pixels- generating in real time. But getting there, it's, it's a very long way to get there. So one of the first step, at Grok Imagine, where I led a small world model team there, was to build video extension. So, video extension- it's the first step of interactivity. Yeah. It's, it's the first step. Yeah. So it's the first step- You have it here, video editing, yeah. Yeah. Yeah. So the first step is because, this unlocks long horizon videos. Typically, for most of the video generation models, you give it a prompt or an image as an initial frame. You generate video, that's it. That's just, one time, done. And some creators would try to, use the last frame as a first frame for the second video. It can-- sometimes it works, but if you do it a few times, it says the quality would decrease. And- It doesn't have that context- Yeah over the full video, so the temporal- Yeah, exactly. Yeah, ‘cause you only gave it the last frame, of course, right? Yeah. Exactly. And- it's actually a pretty fun hack. if you've seen like- Oh, no, he's saying something better. Yeah. And for example, like Vue, I remember Vue 3 has like a second context of the last video. It is slightly better than using the last frame, but it has the same problem-- similar problem that it, the quality would decrease. if you extend a few times to, one minute, the video quality would look much worse than the first video. Second, another problem is that the model doesn't have long-range knowledge of, what's happening before. Say, if they generate some dialogue, some, two people speaking, and their voice might change, over some time, especially if the second conditioning, it does not cover the previous context. So these are the core challenges. So the Grok Imagine video extension, it has historical context of all of the previous generated videos. It can, It has, it has the context of, who is speaking and what objects have appeared and everything, having that to generate the next video. So if we naively do this, you can imagine, just, put all of the previous history video tokens into the context. The context lens will easily explode. Especially for video models, that can be like a few, a few million context, I would imagine- context lens. Yes.Yeah.Swyx [00:54:58]: Let's run with that.Ethan [00:54:59]: for example, like in Cosmos, I think just five seconds of video is like a fifty K or sixty K number of tokens. So like if you do, if you do fifty second, that's a five hundred K tokens. If you do longer than that, easily explode. This long horizon, problem was the first step we're trying to solve world model. It turns out people, yeah, people love video extension. Like a lot, a lot of the creators love using video extension to create longer form videos. This is the part I liked that you have a, you have an intermediate step toward the final goal instead of just a straight shot to the final version very much.Swyx [00:55:48]: But I can see you have a strong vision of where we want to end up.Long Context, Redundancy, and Efficient Interactive VideoVibhu [00:55:51]: Does it seem like it's an efficiency issue? okay, we're at a few million tokens context,. If you draw the parallel to language models, we had very short context, two thousand, eight thousand, then, you scale it up one million, ten million. sure, there's effective context, but at the end of the day, it's just what's it worth? sure, there's a whole training data side. In video, it might be slightly easier ‘cause we have a hundred million token video, right? Just take a movie with the full context there. Like is this efficiency from an inference standpoint that like it's expensive, but we know how to solve it? Or like why is this not the approach? So like my broader point was on your second point of world models, you say it needs to be interactive and live, right? You should be able to play a game and see the interaction live. So one thing I see with research is a lot of what you actually serve is different than what you build, right? So we talked about distillation. You train big model, you distill it, you do quantization, speculative decoding. We do all this stuff to serve it efficiently. Should we not just have a solution, like a world model that can interact well, do inference optimization, serve it, distill it secondary, so make it real time after you solve it? So like a-- another parallel is say, continual learning, right? What we need is someone to solve it and show it works inefficiently. Give it a few years, people will make it efficient. Same thing with regular attention, right? It worked. Over a few years, people have different forms of attention, and we've scaled it to be efficient at log context,? So kind of two things there, right? One is it seems like it works. You've scaled it. Can we not just scale it a lot more efficiently over time? Do we need a separate approach if this works? And same thing with interaction, right? if we can get it done, like if we can solve some way that it works, we can solve making it more efficient from an inference standpoint later.Ethan [00:57:53]: that's actually a very good point. So in videos, there's actually a lot of redundancies. So we solve a lot of the pixel redundancy from VE, but there's more redundancy in long range and long horizon videos. Say, if a character appear in the first clip and then it disappeared, it only reappear at the end of the video, you probably don't need the-- the context, like in the middle of the generation. So you only need that character, where you need. So that's why, I helped build another feature. It's a reference video.Vibhu [00:58:36]: Is it here?Swyx [00:58:36]: is it the same model release or different one?Ethan [00:58:39]: It's a different one.Ethan [00:58:41]: You probably need to search onSwyx [00:58:43]: I'll find itEthan [00:58:43]: X reference to video.Ethan [00:58:46]: So reference video allow you to like upload up to seven images as condition and generate the video. Say, if like I want-- it can, it can be characters or objects or even scenes. Say like I want, I want condition on, Sean's selfie and holding a bladeSwyx [00:59:07]: We have a dogEthan [00:59:08]: or whatever.Swyx [00:59:08]: We put the dog in the thing.Ethan [00:59:09]: you can put them there and the video models will generate the video from and copies the context over. So that can solve a lot of the problems there, like the long context problem. It doesn't need to have a very long context, but it's-- I feel like it's an intermediate solution. The modelSwyx [00:59:29]: It's cheating.Ethan [00:59:30]: the model should be able to like selectively know, where should I draw the references. So say if I want to generate a movie, I generate it autoregressive, like a ten second at a time or something. And now this character appear, I can look back to where it first appear and, bring that back. Yeah, this one, I put the references. Yeah, that's, Optimus, Einstein myself, Annie.Vibhu [01:00:02]: Oddly enough, I used Grok Search to find it, and it pulled your LinkedIn post. But yeah we found it.Ethan [01:00:08]: Interesting.Vibhu [01:00:10]: ButxAI's Underrated Work, Culture, and WatermarkingSwyx [01:00:11]: this is a problem. This is not your fault, but like XAI doesn't communicate all this work that you do very well because they just have the model release and then that's it. But actually, these details are very good.Swyx [01:00:22]: As far as I understand, everything you just described is state-art, like no one else has done it.Vibhu [01:00:30]: A lot of-- yeah, I have a lot moreSwyx [01:00:32]: And then, and then you just put this blog post with the cookies. I'm this is not enough,?Swyx [01:00:37]: but I, obviously this is like the high level numbers that people want to know. But no, okay, soVibhu [01:00:42]: And I wonder, like part of that is also some labs don't share research into what happens. And ifSwyx [01:00:50]: No, but this is literally bragging about how good they are, right?Swyx [01:00:54]: Like, why would you not say that you are capable of extending with full context? this is not a secret sauce. This is like we did the work. yeah, I don't know.Ethan [01:01:02]: different labs have slightly different communication styles.Swyx [01:01:07]: Anyway, if anyone from XAI is listening we are always happy to help you tell your story. Yeah, okay, so you did references, and I think, I think kind of the point you're, you're making is it is sort of like a kludge, right? this is-- you can do seven, but what about 100?Swyx [01:01:23]: Right? Then you need a completely different thing.Ethan [01:01:26]: So I think it's-- this is, a mechanism to, select the context from the history, and you might not put the entire history into the context. for example, there's a paper called Frame Pack, which haveEthan [01:01:41]: a heuristic that the latest history, the last one second, I put the entire history, and the history before that, I would, compress it and makes the video smaller. So they follow this pattern, this build overall pattern that the maximum sequence length is fixed. So the further you are from the current frame, you have a smaller image. So this is just a heuristic. I think it can be more automatic. The model is aware like which history part of it can be select. So this part of the research is actually being actively, worked on by a lot of people. It's also quite interesting. I feel this is actually, this part of long context is a little bit ahead of the LLM part.Ethan [01:02:31]: So for example, like in LLMs, if you-- so contexts keep growing. Let's say if you call tool and the tool call history is extremely long, that's still in context, and keep growing, keep growing. Even if you switch the topic to something else, the whole context was there. There are some agentic harnesses that help you to, say, prune the tool results and, prune Like when you, when you query a file, only show like the top 200 lines or something. Those were very heuristic-driven.Swyx [01:03:08]: For listeners, we did a write-up on the cloud code, leak where there are eight different kinds of pruning, including like you prune the tool results and all that. So you can, you can read up on that kind of thing.Ethan [01:03:17]: I think, one breakthrough in continual learning might be like a way to automatically, manage its own context.Swyx [01:03:27]: These are all heuristics, and they will be replaced by machine learning.Ethan [01:03:30]: InterestinglyVibhu [01:03:32]: TheEthan [01:03:32]: the same thing is being researched in both LLMs and video models.Vibhu [01:03:36]: The interesting thing is also like in the paper you showed, it's actually happening at the model level, right? Compared to like language models, sure, we have base attention, but we'll do our own compression, we'll do our own pruning, which is separate from model error.Vibhu [01:03:49]: Eventually, it all just boils in, hopefully.Swyx [01:03:52]: I think this is a form of like attention, but like also know sort of reasoning attention. I feel like that's different than normal attention.Swyx [01:04:03]: Does that, does that make sense?Ethan [01:04:04]: It's, it's different in the sense that attention, not to mention, set sparse attention aside,

What happens when a successful oncologist leaves clinical practice to help develop cancer treatments on a global scale? In this episode of The Lebanese Physicians Podcast, Dr. Safi Shahda shares his journey from academic oncology to leadership roles at Eli Lilly, Intellia Therapeutics, and AstraZeneca. We discuss career transitions, pharma misconceptions, innovation, AI in drug development, mentorship, and how physicians can expand their impact beyond the bedside. #LebanesePhysiciansPodcast #PharmaCareers #Oncology #ClinicalResearch #DrugDevelopment #MedicalLeadership #PhysicianCareer #AstraZeneca #Biotech #HealthcareInnovation #ArtificialIntelligence #CancerResearch #MedicalEducation #CareerGrowth #PhysicianLife #Medicine #Subscribe #Podcast #Healthcare #clinicaltrials ⁨@thelebanesephysicianspodcast⁩   ⁨@astrazeneca⁩  On all podcast apps Website: https://thelebanesephysicianspodcast.podbean.com

In this episode of the The Grad School Femtoring Podcast, I talk with Dr. Leslie Wang about writing authentically, values misalignment in academia, and choosing a career path that feels aligned with your long-term wellbeing. This episode is for anyone who feels exhausted by the pressure to constantly perform, produce, and prove themselves while questioning whether their current path still reflects who they are and how they want to live. We explore how academia, like many professional spaces, can shape people into prioritizing external validation over internal alignment, and how signs like dread, resentment, perfectionism, burnout, and comparison often point toward deeper values misalignment. Dr. Wang shares how she transitioned from a tenured professor to a coach supporting scholars with writing, publishing, and career decisions rooted in values-alignment. We also discuss how graduate students can approach career exploration more intentionally, how to identify your internal compass, and how to write for real readers instead of only writing for gatekeepers. In this episode, you will learn: How to identify early signs of values misalignment in academia Why external achievement alone often does not create long-term fulfillment How core values can guide career decisions and sustainable work practices Ways to approach writing more authentically while maintaining scholarly rigor How to identify an ideal reader beyond your dissertation committee or reviewers Why graduate students benefit from considering multiple career paths instead of defaulting to the tenure track Work with me If your institution, organization, or team is looking for workshops on sustainable productivity, executive functioning, leadership development, or culturally responsive student support, learn more here: https://gradschoolfemtoring.com/speaking/ Learn more about my coaching services for graduate students and professionals: https://gradschoolfemtoring.com/coaching/ Connect with Dr. Leslie Wang Your Words Unleashed: https://yourwordsunleashed.com Dr. Wang on LinkedIn: https://www.linkedin.com/in/leslie-k-wang-phd-a813227/ Free resource Download your Grad School Femtoring Resource Kit: https://gradschoolfemtoring.com/kit/ Explore more Listen to more episodes on Personal Development and Mindset: https://gradschoolfemtoring.com/podcast_catergory/personal-development-and-mindset/ Support the podcast with a one-time or monthly donation: https://donate.stripe.com/bJedR8dGRcs6ewGdwq38401 Access transcripts and additional resources: https://gradschoolfemtoring.com/podcast/ Audio and transcript edited by Yessi Sanchez: https://www.linkedin.com/in/yessisanchez/ This podcast is a proud member of the Genuina Media network. The Grad School Femtoring Podcast is for educational purposes only and is not a substitute for therapy or other professional services. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Careers rarely move in a straight line, and for many people, the old roadmap no longer works. In this special feed drop from LinkedIn's Hello Monday, host Jessi Hempel sits down with Caroline Wanga for a conversation about building a career with intention, authenticity, and room to evolve.As president and CEO of Essence Ventures and co-founder of Wanga Woman, Caroline has spent years helping people rethink success on their own terms. Before leading Essence, she spent 15 years at Target, rising from intern to the C-suite — a journey that taught her the value of continuously revisiting, reshaping, and even reinventing your career map.Jessi and Caroline discuss:Why your “next right move” may not look like anyone else'sWhy playing with your career map, not perfecting it, leads to clarityHow, and when, Caroline has created her own career mapsThe role of authenticity in leadership and lifeWhat it really takes to live and work with personal purposeTools and mindsets for building a meaningful career in today's worldCaroline also shares insights from her memoir, I'm Highly Percent Sure, and offers a refreshing perspective for anyone questioning what comes next.Follow Jessi Hempel and Caroline Wanga on LinkedIn, and listen to more Hello Monday wherever you get your podcastsFor the full text transcript, visit https://www.ted.com/podcasts/worklife-transcripts Hosted on Acast. See acast.com/privacy for more information.

SummaryWhat happens when passion for storytelling meets the entrepreneurial spirit? In this episode of the Startup Junkies podcast, Danielle Keller, media entrepreneur, award-winning podcaster, and editor-in-chief of Northwest Arkansas's beloved Peekaboo magazine, joins Daniel Koonce, Caleb Talley, and Ty Steele for a conversation packed with inspiration, nostalgia, and the realities of building community through storytelling.Danielle shares her fascinating career journey, from her beginnings in California writing for school papers, through a detour in higher education, to diving fearlessly into documentary film, video production, and ultimately acquiring and revitalizing Peekaboo magazine. She details how Peekaboo, once a crucial parental resource before the rise of social media, became a passion project resurrected through grit, research, and community demand. The print magazine's unique sensory experience illustrates the hunger for tangible connections in a digital age.Listeners will delight in anecdotes about local mascot Ozzy the Ozark Fox, created by Danielle's daughter, and how family, authenticity, and real community voices shape every issue. The episode highlights the importance of collaboration, adaptability, and embracing both print and digital platforms as Peekaboo grows and evolves.With future visions of podcasts, dynamic web offerings, and newsletters, Danielle reminds us that it's never too late to pursue fresh dreams, amplify others' voices, and savor the present. This episode is a must-listen for entrepreneurs, storytellers, and anyone who believes in the lasting power of local stories!Show Notes(00:00) Danielle's Career Path in Media(04:06) Starting Peekaboo for Parents(09:48) Evaluating Print Magazine Revival(18:10) Creating a Themed Editorial Calendar(20:37) Seasonal Advertising Opportunities(23:07) Expanding Digital Content(33:21) Closing ThoughtsLinksDaniel KoonceCaleb TalleyTy SteeleStartup JunkieStartup Junkie YouTubeDanielle KellerPeekaboo Magazine

Collin Werner might only be 27, but don't let his age fool you. He's already worked in some of the most well-regarded kitchens in Omaha, now advancing to become the Chef de Cuisine at Au Courant. We run through Collin's career, assess the lessons he learned at different stops, discuss the value of culinary school, and more! This episode is a deep dive into creativity, discipline, and what it really takes to succeed in a modern kitchen.

what does it actually take to build a career you're excited about? in this episode, the hot pursuit girls discuss navigating the throes of corporate america and pursuing passions that don't always fit the traditional path. they share their experiences forging major career shifts throughout the years and how learning to bet on yourself opens the door to a career that is one step closer to fulfilling your dreams and passions. 0:00 overview of hp girls' corporate backgrounds 12:14 what lies were you sold at the start of your corporate career? 20:55 when did you realize corporate was a game? 25:55 planning your escape out of the corporate hole 33:00 is there a time to stay the course? 40:53 after you decide you want it, how do you get there? 50:56 common mistakes in making career jumps 54:25 last words of advice for forging your path CONNECT WITH US Connect with us @thehotpursuitpod on Instagram/TikTok/Youtube.  Email us at ⁠hello@thehotpursuitpod.com⁠.  Learn more at ⁠thehotpursuitpod.com⁠. THE HOT PURSUIT PODCAST: Hosted and written by: Jennifer Han, Emily Lin, and Madelyn Ong Produced by: Hot Pursuit Media and AsianBossGirl Edited by: Sutton Dreher of Adler Grey Videography Theme song composed by: Shawn Halim Art by: Kelsey Cordutsky Motion Graphics by: Matt Ebling Learn more about your ad choices. Visit megaphone.fm/adchoices

Welcome back to the Law School Toolbox podcast! Today we're talking about the legal jobs for which grades matter a lot, where they matter somewhat, and where they really don't matter much at all. We also share some tips for what you should do if your grades so far aren't what you had hoped for. Thanks to Juno for sponsoring this episode! If you're thinking about student loans for law school, head to JoinJuno.com to explore your options and see how Juno can help you find a better rate.    In this episode we discuss: Where grades matter a lot Where grades matter somewhat Where grades matter less than you think What if your grades are disappointing so far? Resources: Career Help with CareerDicta (https://lawschooltoolbox.com/careerdicta/career-help/) JoinJuno.com (https://joinjuno.com/) Podcast Episode 9: How To Raise Your Grades as a 2L or 3L (https://lawschooltoolbox.com/podcast-episode-9-raise-grades-2l-3l/) Podcast Episode 28: Dealing With Bad Law School Grades (https://lawschooltoolbox.com/podcast-episode-28-dealing-bad-law-school-grades/) Podcast Episode 44: How to Get a Judicial Clerkship (https://lawschooltoolbox.com/podcast-episode-44-how-to-get-a-judicial-clerkship/) Podcast Episode 98: Top 1L Questions: Non-Traditional Law Students (https://lawschooltoolbox.com/podcast-episode-98-top-1l-questions-non-traditional-law-students/) Podcast Episode 101: Preparing for a Career in Public Interest Law (With Ashley Matthews of Equal Justice Works) (https://lawschooltoolbox.com/podcast-episode-101-preparing-career-public-interest-law-ashley-matthews-equal-justice-works/) Podcast Episode 116: Life as a Small Firm Associate (With Jeremy Richter) (https://lawschooltoolbox.com/podcast-episode-116-life-as-a-small-law-firm-associate-with-jeremy-richter/) Podcast Episode 176: Talking About Judicial Clerkships with Kelsey Russell (https://lawschooltoolbox.com/podcast-episode-176-talking-about-judicial-clerkships-with-kelsey-russell/) Podcast Episode 521: Smarter Borrowing: How Juno Helps Lower Student Loans (https://lawschooltoolbox.com/podcast-episode-521-smarter-borrowing-how-juno-helps-lower-student-loans/) Download the Transcript  (https://lawschooltoolbox.com/episode-556-how-much-do-grades-matter-for-different-career-paths/) If you enjoy the podcast, we'd love a nice review and/or rating on Apple Podcasts (https://itunes.apple.com/us/podcast/law-school-toolbox-podcast/id1027603976) or your favorite listening app. And feel free to reach out to us directly. You can always reach us via the contact form on the Law School Toolbox website (http://lawschooltoolbox.com/contact). If you're concerned about the bar exam, check out our sister site, the Bar Exam Toolbox (http://barexamtoolbox.com/). You can also sign up for our weekly podcast newsletter (https://lawschooltoolbox.com/get-law-school-podcast-updates/) to make sure you never miss an episode! Thanks for listening! Alison & Lee