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Inside AsembleAI: DeepTech, AI & Science: Daily Summaries Delivered
by Mac & Sam
AsembleAI brings you thought-provoking conversations at the nexus of artificial intelligence, innovation, and leadership. In each episode, hosts Mac and Sam, veterans in data and tech world, sit down with AI researchers, fast‑scaling founders, Fortune 500 executives, and pioneering technologists to reveal how AI is reshaping business strategy, sparking breakthrough product development, and guiding executive decisions. Tune in for actionable insights, compelling case studies, and forward‑looking perspectives on the promises and pitfalls of AI‑driven innovation.
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- 2 weeks ago44 min
EP 52: Shadow AI: Why $10.3M is Costing Your Organization More Than You Know
Shadow AI is costing organizations $10.3 million a year—more than malicious insider threats combined. Employees are using AI tools nobody approved, on data nobody's tracking, and most leadership teams have no idea it's happening at this scale. Banning AI doesn't work. You can't solve this with another policy PDF nobody reads. You need real behavioral change. In this episode, hosts Sam Dey and Mac Goswami sit down with Kate Marshall-founder of TheGrai and author of AI at Work—to expose why most enterprise AI rollouts fail at the most critical layer: getting people to actually adopt and stick with new tools and processes. What You'll Learn: 🔹 The $10.3M Shadow AI Problem — What that number actually represents and why banning AI just drives it underground 🔹 The Maturity Model Trap — Why organizations get stuck between Level 1 (Awareness) and Level 2 (Shadow AI), with leadership presenting vendor demos while employees silently use unapproved tools 🔹 Why Generic Training Fails — The fatal flaw of all-hands lunch-and-learn sessions and what role-specific, sticky AI training actually looks like in practice 🔹 The Habit Layer™ Framework — Kate's proprietary methodology for turning one-time training into lasting behavior change 🔹 Data Hygiene as the Foundation — Why cleaning up your downloads folder, emails, and redundant files is where AI transformation actually begins 🔹 The Book: AI at Work — Why Kate wrote a 3-chapter workbook for non-technical professionals instead of another theory-heavy guide Kate's Closing Insight: "Adoption is not a training day. It's a habit. You have to give employees not just access to tools, but time, space, and role-specific guidance to actually learn how to use them." Key Takeaway: The gap between knowing about AI and actually using it effectively is the difference between organizations that transform and those that waste millions on failed pilots. Connect with Kate Marshall: Website: katemarshall.ai LinkedIn: https://www.linkedin.com/in/kate-b-marshall/ Book: AI at Work Subscribe: Spotify | Apple Podcasts | Amazon Music | iHeart Radio | YouTube #ShadowAI #AIAdoption #HabitLayer #AIatWork #ChangeManagement #EnterpriseAI #AsembleAI
- Jun 21, 202636 min
EP 51: AI-Native Software Development: Building Production Systems with Multi-Agent AI
"AI native software development" gets thrown around everywhere right now—and almost nobody can define it clearly. Not a chatbot bolted on. Not Copilot autocomplete. We mean production-grade systems where AI agents write, orchestrate, and ship the work end-to-end. In this episode, hosts Sam Dave and Mac Goswami sit down with Mohamed Faker, Engineering Leader, Financial Services AI at Vanguard Group and co-founder/CTO of Hirin, a fractional leadership hiring platform built almost entirely by orchestrating specialized AI agents. Key Insights: What AI-Native Actually Means — Every line of code in Hirin was AI-produced. Mohamed's role: architect, decision-maker, final say on direction—not the one typing code. From Solo Orchestrator to Manager of Agents — How he evolved from manually prompting individual AI chats (architect, UX expert, engineer) to building agent hierarchies with sub-agents and dedicated "audit" agents reporting directly to him. Where Agents Fail — Spotting when an agent burns tokens without progress, takes conversations sideways, or simply isn't suited to the task—and knowing when to stop. Validation at Scale — Building internal "audit department" agents that verify other agents did exactly what was asked, nothing more, nothing less. Product Management Is the New Core Skill — Knowing how to break down features, prioritize by dependency and complexity, matters more than knowing how to code. Biggest AI Adoption Mistakes — Rushing to adopt AI without defining real ROI, plus strategies that fail because the workforce isn't trained or willing to execute them. Human-AI Collaboration — Why the human must always stay in the loop as critical thinker and decision-maker, even as the agent-to-human ratio shifts dramatically. The Horse-and-Carriage Analogy — Entire industries can disappear in 15 years, but the people who adapted earned more by managing the new technology rather than resisting it. Mohamed's takeaway: "The future is you managing a subset of AI agents. Think about it-you're going to have multiple versions of yourself working together." Connect with Mohamed Faker: https://www.linkedin.com/in/mohamed-faker/ Check out Hyern: https://hyern.com/ Subscribe: Spotify | Apple Podcasts | Amazon Music | iHeart Radio | YouTube #AINative #MultiAgentAI #SoftwareDevelopment #AIAdoption #ProductManagement #AsembleAI
- Jun 10, 202644 min
EP 50: AI & Cybersecurity: Building Agentic AI for Real-World Threat Detection
97% false positives. Millions of alerts daily. Security tools that can't keep up. The threat landscape has outpaced traditional security operations—and Agentic AI is the answer. In this episode, hosts Mac Goswami and Sam Dey sit down with Ramya Ganesh, Top 50 Women Cybersecurity Leads in the US and AI leader at Cisco, to break down how autonomous AI agents are transforming cybersecurity from detection to response. Key Insights: Multi-Agent Systems Beat Single Models — Like a hospital with specialists, multiple focused agents outperform one generalist AI. Modular, scalable, explainable, resilient. The Future SOC — Not humans vs. AI, but humans supervising teams of AI agents handling continuous telemetry while analysts focus on strategic decisions. Agentic AI vs. AI-Assisted Tools — Speed, autonomy, and cross-system correlation distinguish today's agentic platforms from yesterday's alert dashboards. POC to Production — Most AI initiatives fail because they start with technology, not business problems. Success requires measurable metrics and governance discipline before deployment. For Women in Tech — Stay curious, experiment, share what you build publicly. Imposter syndrome is real but community and visibility accelerate growth. Ramya's takeaway: "The companies seeing the greatest AI success aren't those with the most advanced models—they're the ones with the strongest discipline around AI adoption." Connect with Ramya: https://www.linkedin.com/in/ramya-ganesh-082bb231/ Subscribe: Spotify | Apple Podcasts | Amazon Music | iHeart Radio | YouTube | Substack #AgenticAI #Cybersecurity #WomenInTech #SOC #AsembleAI
- May 25, 202651 min
EP 49: How to Spot the Next Healthcare AI Fraud Before It Happens
What does it take to call out billion-dollar healthcare AI companies when the system is rigged against whistleblowers? In this episode of Inside Assemble AI, hosts Sam Day and Mac welcome Sergei Polevikov, PhD-trained data scientist, AI entrepreneur, author of the widely-read Substack newsletter AI Health Uncut, and co-host of Digital Health Inside Out. Sergei has spent years investigating irregularities in healthcare AI, from inflated product claims and misleading adoption reports to the structural VC incentives that allow fraud to fester. This is one of our most candid conversations yet — covering the 10 patterns that predict healthcare AI failure, why the real AI adoption rate in healthcare is nowhere near what industry reports claim, and why human-in-the-loop remains an essential safeguard regardless of how capable foundation models become. TOPICS COVERED: → How Sergei went from healthcare AI founder (WellAI / Chart2Chart) to fraud investigator — and why transparency, not scandal, drives his mission. → His 10 healthcare tech failure patterns, including: the Chinese wall between management and teams, investors-as-customers conflicts of interest, smoke-and-mirrors technology, champagne-and-cocaine financial mismanagement, toxic code of silence, founder extortion, and celebrity protection schemes. → Why surveys from firms like Menlo Ventures and McKinsey dramatically overstate AI adoption — and what US Census Bureau data covering 30,000+ smaller healthcare organisations actually shows. → The structural reason why incumbents like Epic, Optum, and Cigna are disincentivised to build genuinely innovative AI products — and why startups like Abridge are winning despite the odds. → What's genuinely working in healthcare AI right now: AI scribes (done well), drug discovery, genomics, and protein structure modelling. → His advice for founders entering the healthcare or pharma space: protect your mission when VC money arrives, read every clause in your operating agreement, and choose partners who care about patients — not just their LPs. RESOURCES & LINKS: 1. "AI Health Uncut" Substack: FixHealth.ai 2. Advancing AI in Healthcare: A Comprehensive Review of Best Practices: https://www.sciencedirect.com/science/article/abs/pii/S0009898123003212 3. "Digital Health Inside Out" podcast: https://www.youtube.com/@DigitalHealthInsideOut CONNECT WITH ASSEMBLE AI: Subscribe on Apple Podcasts, Spotify, iHeartRadio, and Amazon Music. Follow our YouTube channel and Substack newsletter for more deep dives into AI's real impact across industries. Have a topic you'd like us to explore? Reach out — we welcome new voices and fresh perspectives. Keywords: healthcare AI, AI fraud, digital health, VC pump and dump, Babylon Health, Olive AI, Theranos patterns, AI scribes, Epic health, healthcare startup, AI adoption, human in the loop, AI compliance, healthcare innovation
- Apr 30, 202614 min
EP 48: AI Transforms Soccer: Premier League Analytics Revolution
68.5 billion euros in EPL betting annually. 1.4 million data points per match. Soccer sits at the absolute center of the AI revolution, and it's transforming the world's most popular sport from officiating to tactical analysis. In Episode 2 of our "AI in Sports Analytics" series, hosts Sam Dave and Mac Goswami explore how AI fundamentally changed soccer from 2020-2025. Revolutionary Technology: Semi-Automated Offside Detection (EPL 2024-25): Calibrated cameras + AI algorithms measure player positions with centimeter-level precision. Pioneered at 2022 Qatar World Cup, now standard across elite leagues. Processes data faster than humans, eliminating decades of controversial calls. Player Tracking: Optical systems track each player 25x/second, detecting invisible tactical patterns. Game-changer: Standard TV footage now generates tracking data previously requiring expensive dedicated cameras. Smaller-budget teams access insights once reserved for Barcelona, Manchester City, Bayern Munich. Match Prediction: 69-78% accuracy with ensemble models. Challenge: Soccer is harder to predict than basketball/baseball due to lower scoring and higher randomness. One lucky deflection can decide a match despite dominating possession. Real-World Impact: Tactical Analysis (March 2025 study): Real-time computer vision tracks all players, ball, formations simultaneously. Coaches see which tactical adjustments opponents made in the 67th minute three weeks ago and how they affected passing networks. Large Events Model (2024): Deep learning framework simulates games from any state. Test tactical approaches against AI-simulated opponents before stepping onto the pitch. Economic Impact: Sports analytics market: $1.03B (2024) → $2.61B (2030). AI-powered betting analytics provide sophisticated predictions. The Reality: AI reveals tactical sophistication fans never saw. That perfect through ball required reading three defenders' positioning, understanding striker's running profile, executing with millimeter precision. AI helps us see genius, not replace it. Subscribe: Spotify | Apple Podcasts | Amazon Music | iHeart Radio | YouTube Next: Baseball AI revolution #SoccerAnalytics #AIFootball #EPL #SportsAnalytics #AsembleAI
- Apr 29, 202616 min
EP 47: AI Revolutionizes Basketball: NBA Analytics 2020-2025
1.4 million data points per game. NBA teams now track every player movement, defensive rotation, and shot attempt with AI-powered analytics—and it's transforming professional basketball in real-time. In this first episode of our "AI in Sports Analytics" series, hosts Sam Dey and Mac Goswami explore how the NBA and WNBA embraced AI more aggressively than any other league. Game-Changing Technology: SportVU Tracking System captures 29 data points per player, tracking all 22 players 10x/second and the ball 25x/second. Second Spectrum uses computer vision to extract data directly from broadcast video—no specialized cameras needed. NBA-AWS Partnership (Oct 2025): "Inside the Game" platform turns billions of data points into compelling insights, introducing AI-powered stats measuring performance never quantified before. Game Prediction: 87% accuracy with ensemble machine learning models (up from 65-70% five years ago). Models now weight three-point efficiency and spacing metrics heavily since the game evolved post-2015. Real-World Impact: Boston Celtics (2024-25): AI models refined defensive schemes using spatiotemporal data, contributing directly to playoff success. Golden State Warriors: Physical AI robots assist practice—rebounding, passing drills, simulating defensive plays. Steph Curry: "Robots provide consistent data-driven feedback humans can't match." Philadelphia 76ers: Large language models now participate as "a vote in any decision"—draft picks to game strategies. Broadcast Revolution: AWS Play Finder analyzes thousands of games, retrieving similar plays in milliseconds. Expected Field Goal models account for defender positioning, pressure, fatigue—not just distance. The Reality: AI predicts trends exceptionally well, but human elements—leadership, clutch performance, chemistry—resist quantification. 87% accuracy doesn't eliminate competitive balance when base-level data is universally available. Subscribe: Spotify | Apple Podcasts | Amazon Music | iHeart Radio | YouTube Next: Soccer/Football AI revolution #NBAnalytics #AIBasketball #SportsAnalytics #NBAtech #AsembleAI
- Apr 28, 202619 min
EP 46: New Collar Jobs: Emerging Roles That Only Exist Because of AI
143% growth for AI Engineers. 136% for Prompt Engineers. 135% for AI Content Creators. These aren't niches—they're fundamental new careers that couldn't exist before AI. In this final "Who Survives the AI Shift" episode, Sam Dey and Mac Goswami reveal 16 brand-new job titles from 2025: Knowledge Architect, Orchestration Engineer, Conversation Designer, Human-AI Collaboration Leader. Top Emerging Roles: Prompt Engineer ($123K avg, top $200K+) - Building systematic AI outputs at scale. 40% fewer hallucinations, 60% better brand alignment. AI Model Trainer - Fine-tune algorithms. Requires technical skills + deep industry knowledge. AI Ethics Officer & Safety Analyst - Critical for governance in regulated industries. Assess biases, develop risk protocols. Data Curator - Most accessible entry point. Domain expertise matters more than degrees. Conversation Designer/NLP Engineer - Build chatbots, virtual assistants, translation systems. AI Product Manager - Bridge technology and business with deep AI understanding. AI Program/Project Manager - Handle AI implementation, operations, budgets. Huge growth projected. Where Jobs Are: Big Tech (Google, Microsoft, Amazon), AI-Native (OpenAI, Anthropic), Traditional Enterprises (JPMorgan, hospitals, retail) The Reality: New collar jobs exist at AI capability + human necessity intersection. Better AI needs MORE human oversight, not less. Consulting and freelancing booming—work that took days now takes hours. The future belongs to those treating AI as collaborative tool, not competitive threat. Subscribe: Spotify | Apple Podcasts | Amazon Music | iHeart Radio | YouTube | Podbean #AIJobs #PromptEngineer #FutureOfWork #AsembleAI
- Apr 22, 202620 min
EP 45: Reskill or Perish? How to Future-Proof Your Career Against AI | Inside AsembleAI
50% of employees need reskilling by 2026-RIGHT NOW. Are you ready, or already falling behind? In this critical episode of "Who Survives the AI Shift," hosts Sam Dave and Mac Goswami expose the brutal reality: only 49% of employees feel equipped for their roles (down from 59% in 2024). Gen Z confidence crashed 20 points to 39%. The gap between awareness and action is where careers die. Key Takeaways: The Training Disconnect: 37% of employers claim they offer reskilling programs Only 28% of employees confirm these exist Companies check boxes without ensuring actual completion Skills That Matter for 2030: AI & big data, cybersecurity, technological literacy Creative thinking, resilience, curiosity Winning combo: Technical fluency + human capabilities AI can't replicate Subscribe: Spotify | Apple Podcasts | Amazon Music | iHeart Radio | YouTube | Podbean #Reskilling #AICareer #FutureProof #Upskilling #DataLiteracy #LifelongLearning #AsembleAI
About Inside AsembleAI: DeepTech, AI & Science
AsembleAI brings you thought-provoking conversations at the nexus of artificial intelligence, innovation, and leadership. In each episode, hosts Mac and Sam, veterans in data and tech world, sit down with AI researchers, fast‑scaling founders, Fortune 500 executives, and pioneering technologists to reveal how AI is reshaping business strategy, sparking breakthrough product development, and guiding executive decisions. Tune in for actionable insights, compelling case studies, and forward‑looking perspectives on the promises and pitfalls of AI‑driven innovation.
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