The AI-First Business Podcast

He bought Amazon gift cards out of his own pocket to hit his KPIs. That's what metric obsession looks like at Revolut.Pierre Cahuzac is a product leader at Europe's most valuable fintech—and explains why their alumni start companies at ridiculous rates. It's not the dashboards. It's the ownership model.You'll walk away with Revolut's framework for organizing teams around metrics instead of code, the P50 vs P10 methodology for identifying human vs machine performance gaps, and why artificial deadlines create throwaway work.Six years at Revolut. Multiple product rescues. One core insight: when you own KPIs instead of features, you start thinking like a founder.Pierre breaks down the "minimum path to value" framework, how to push back on impossible targets from executives, and why their country expansion machine beats custom local builds.Plus: the real reason Revolut produces so many entrepreneur alumni, and what "onion layer thinking" means for cross-functional leadership.🤝 Connect with PierrePierre Cahuzachttps://www.youtube.com/watch?v=r1INVEjqo3M Ep. 37 Getting It Done: RB5 and the future of business finance Revolut Business 5⏱ Episode Chapters(00:00) From film director to Revolut: The unconventional path(06:14) Why film financing was too slow for innovation(10:27) How Revolut’s metric obsession works in practice(15:08) Building products with global vision, local execution(17:49) When ambitious targets almost broke the team(26:00) The Revolut Mafia: Alumni and entrepreneurship(29:07) Patterns in fintech founders(31:33) Future predictions and final thoughts#AIFirstBusiness #ProductManagement #Revolut #FinTech #StartupFounders #MetricsDriven #ProductStrategy

He bet against trillion-parameter models when every VC said he was insane—now NVIDIA is publishing papers proving him right.Sudarshan from Smallest.ai cracked enterprise voice AI by ignoring Silicon Valley's foundation model obsession. While competitors chased infinite compute, his autonomous vehicle background revealed the truth: compact models handling life-or-death navigation decisions outperform bloated systems requiring massive resources.The contrarian thesis almost killed fundraising until one viral launch changed everything. World's fastest text-to-speech hits millions of views, multiple term sheets arrive the same day, $8M seed closes in two weeks. But thousands of curious developers meant zero revenue.The real breakthrough wasn't technical—it was understanding enterprise buyers. Companies don't want your API documentation. They want consultants who solve problems using AI, not AI companies hunting for use cases. The transformation from model provider to solution consultant unlocked Fortune 500 deals.You'll discover the playbook for contrarian deep tech bets, why enterprise AI adoption is messier than anyone admits🤝 Connect with Sudarshanhttps://www.linkedin.com/in/sudarshankamath/ https://www.linkedin.com/company/smallest/ https://smallest.ai https://www.youtube.com/@smallest_aihttps://www.forbes.com/sites/brentgleeson/2025/04/28/how-business-leaders-are-unlocking-ais-full-potential/

Investment banking taught him speed, but Amir Prodensky needed something bigger to build.Fresh from Revolut's hyper-growth chaos, Amir saw the massive gap in financial services customer experience. Insurance customers were stuck with phone queues and paper processes while expecting Amazon-level service.So he built Strada: AI agents that handle every customer interaction across phone, text, email, and chat while maintaining perfect context. These aren't simple chatbots—they connect to core insurance systems and take real actions like generating certificates and processing claims.Amir's framework for AI adoption moves through five stages: from co-pilot tools to fully autonomous agents. The companies moving fastest through these stages are creating unbeatable competitive advantages.His prediction: customer expectations will shift permanently once they experience resolution at AI-speed. Traditional service will feel broken in comparison.🤝 Connect with Amirhttps://www.linkedin.com/company/getstrada/ https://www.linkedin.com/in/aprodensky/ https://www.getstrada.com/ ⏱ Episode Chapters(00:00) From Bay Street to insurance AI agents(01:46) Career pivots: Banking to Revolut's US expansion(05:16) Big bets at Revolut: What worked and what didn't(14:24) Finding Strada: From horizontal automation to insurance-focused AI(21:17) AI-native product development without designers(25:06) Rapid fire: AI tools and expensive experiments that worked🔥 Enjoyed the episode?Drop a like, subscribe, and share it with someone building fast.🎙 **The AI-First Business Podcast**Sharp strategy. Fast builds. Real AI.No panel chatter. No fluff. Just the moves that scale.📺 **Want more?**Search *AI-First Business Podcast* on Spotify, Apple, or wherever you get your podcasts.📲 **Follow us:**Instagram / TikTok / LinkedIn → @aifirstbusinessAll links: [feedlink.link/aifirstbusiness](https://feedlink.link/aifirstbusiness)⚠️ **Disclaimer:** Views are personal and not reflective of any organization. For informational purposes only.#AIInsurance #InsurTech #FinTechFounders #CustomerServiceAI #AIAgents #StartupJourney #InsuranceInnovation #VoiceAI #FinTech #TechFounders

He walked away from aerospace engineering and built a SaaS startup cutting data transformation time by 90%.Kaustav Mitra, co-founder and CEO of Paradime.io, shares how he turned a restless career path into a weapon—building teams with autonomy, betting on AI before the noise, and rethinking how companies manage data at scale.You’ll get clear on:Why autonomy isn’t a “nice to have” but a force multiplier.How AI is reshaping the modern data stack (ETL → ELT → AI-native).The leadership habits that let small teams compete with giants.Before Paradime.io, Kaustav cut his teeth scaling analytics at Revolut and Octopus. If you’re serious about using AI to collapse development cycles and increase business velocity, this one’s non-negotiable.🤝 Connect with Kaustav Mitra & Paradime.iohttps://www.linkedin.com/in/mitrakaustav https://www.linkedin.com/company/paradimelabswww.paradime.io https://www.youtube.com/@paradimelabs⏱ Episode Chapters(00:00) Kaustav’s unconventional career path(03:32) The MBA network that changed everything(06:02) Building Paradime with high-autonomy teams(12:16) Going AI-first with DinoAI(20:18) Leveling the playing field for startups with AI(25:28) ELT evolution and the future of the modern data stack(29:51) Educating customers on AI and reframing accuracy(36:24) Bridging human and machine-readable worlds(40:39) How AI will reshape data, teams, and entrepreneurship🔥 Enjoyed the episode?Drop a like, subscribe, and share it with someone building fast.🎙 **The AI-First Business Podcast**Sharp strategy. Fast builds. Real AI.No panel chatter. No fluff. Just the moves that scale.📺 **Want more?**Search *AI-First Business Podcast* on Spotify, Apple, or wherever you get your podcasts.📲 **Follow us:**Instagram / TikTok / LinkedIn → @aifirstbusinessAll links: [feedlink.link/aifirstbusiness](https://feedlink.link/aifirstbusiness)⚠️ **Disclaimer:** Views are personal and not reflective of any organization. For informational purposes only.

Barr Moses grew up running experiments in her dad’s physics lab. Today, she’s the CEO of Monte Carlo, the leading platform for data + AI observability.This episode goes beyond her founder story. It’s about why data trust is the new currency in AI—and how Barr built the playbook for companies that want to move fast without breaking everything.She’ll show you:Why clean dashboards don’t mean reliable data.The messy middle of making data dependable at scale.The real reasons intuition still matters when the numbers run out.Listen if you want to bulletproof your AI stack from the inside out.Barr Moses is CEO & Co-Founder of Monte Carlo, a data + AI observability company backed by Accel, GGV, Redpoint, and other top Silicon Valley investors. Previously, she was VP Customer Operations at Gainsight, a management consultant at Bain & Company and served in the Israeli Air Force as a commander of an intelligence data analyst unit. Barr graduated from Stanford with a B.Sc. in Mathematical and Computational Science.🤝 Connect with Barr Moseshttps://www.linkedin.com/in/barrmoses https://www.montecarlodata.com/Barr is the author of the book ""Data Quality Fundamentals: A Practitioner's Guide to Building Trustworthy Data Pipelines." More info here: https://www.oreilly.com/library/view/data-quality-fundamentals/9781098112035/ Barr has written many articles, including:https://www.montecarlodata.com/blog-the-past-present-and-future-of-data-quality-management/ https://www.montecarlodata.com/blog-top-3-ai-problems-to-solve https://www.montecarlodata.com/blog-are-we-in-an-ai-bubble/ https://www.montecarlodata.com/blog-6-things-every-cdo-needs-to-know-about-ai-readiness/ https://www.montecarlodata.com/blog-ai-fomo-is-tearing-your-company-apart/ https://www.montecarlodata.com/blog-2026-will-be-the-year-of-data-ai-observability/ https://www.montecarlodata.com/blog-will-genai-replace-data-engineers https://www.montecarlodata.com/5-hard-truths-about-generative-ai-for-technology-leaders/⏱ Episode Chapters(00:00) Barr Moses’ early path from consulting to data(03:41) Lessons from building customer success at Gainsight(06:08) The pain of unreliable data and Monte Carlo’s origin story(08:43) Why data + AI observability must be end-to-end(12:58) The growing complexity of today’s data and AI supply chain(16:03) From data observability to AI observability(20:15) The four root causes of data and AI product failures(24:51) Rebuilding trust after AI incidents go wrong(29:55) When companies realize they need observability in place(33:02) The executive questions shaping AI adoption at scale(38:52) Maturity stages in data quality and observability(43:01) The tipping point for AI in production—and what’s next(45:20) How to prepare for AI experimentation failures(47:38) What keeps Barr motivated building in this space#DataObservability #AIInfrastructure #AIDataQuality #AIObservability #MonteCarloData #BarrMoses #AIProductFailures #DataReliability #AIInsights #AIDecisions #DataInnovation #TechLeadership #AIExecution #AIStartups #AIApplications #DecisionMaking #AIRevolution #DataDriven #BusinessStrategy #AILeadership🔥 Enjoyed the episode?Drop a like, subscribe, and share it with someone building fast.🎙 The AI-First Business PodcastSharp strategy. Fast builds. Real AI.No panel chatter. No fluff. Just the moves that scale.📺 Want more?Search AI-First Business Podcast on Spotify, Apple, or wherever you get your podcasts.📲 Follow us:Instagram / TikTok / LinkedIn → @aifirstbusinessAll links: feedlink.link/aifirstbusiness⚠️ Disclaimer: Views are personal and not reflective of any organization. For informational purposes only.

She spent a decade in public health—and built the product she wished existed.Ludovica’s journey spans biotech, epigenetics, and behavior change. But after years designing obesity interventions, one insight stuck: people don’t need more advice—they need fewer barriers.She founded Reveal My Food to make healthy eating frictionless. It’s the first AI-powered nutrition and food-ordering platform designed for employee health—merging science, behavioral design, and real-time environments.In this episode:Why education doesn’t change eating habits—but context doesHow to build for default choices, not willpowerThe hidden architecture of obesogenic environmentsWhat nutrition tech gets wrong about human behaviorHer goal? Return 100 million years of good health to humanity. And it starts with what’s on your plate—by design.📖 Chapters(00:00:00) Why culture and diet are inseparable(00:03:08) From biotech to public health: Her path to food systems(00:06:33) How society makes healthy eating nearly impossible(00:10:20) The rise of the obesogenic food environment(00:13:18) Why public health interventions fail(00:19:38) From physical to digital food environments(00:22:47) The pitch: What Reveal My Food actually does(00:24:50) How AI personalizes workplace meals(00:29:10) Why staff canteens are the perfect entry point(00:33:10) Building the largest real-world nutrition dataset(00:36:12) What AI unlocks that wasn’t possible beforeConnect with Ludovica de Pieri www.linkedin.com/in/ludovicadepieriConnect with Reveal My Foodhttps://www.revealmyfood.com/https://www.youtube.com/@Revealmyfoodhttps://www.instagram.com/revealmyfood/

"Culturalization isn’t just better marketing—it’s survival."What’s the right way to scale globally with AI? Is localization enough—or do you need cultural depth to win trust and customers?This episode is part of our **Revolut Mafia Series**, spotlighting the 100+ alumni now building some of the sharpest AI startups in tech.Ashish and Ahmed, co-founders of Native (ex-Revolut), debate what it takes to break into new markets when AI drops the cost of experimentation to near zero. Ashish argues for deep culturalization—reshaping content for meaning, not just translation. Ahmed says simplicity scales better—and over-culturizing adds bloat and risk.This episode covers:-How AI is redefining global marketingWhen localization fails (and why)What Revolut taught them about fast shipping and market trustThe rise of technical founders as AI-native operatorsIf you're building a startup, launching in multiple markets, or rethinking your GTM strategy—this is required viewing.📖 Chapters(00:00:00) From Revolut to Nativ: Founders’ origin story(00:02:17) Why global marketing is broken—and what Nativ fixes(00:06:39) AI-native founding: How building companies just changed(00:10:26) Why VCs want technical founders now(00:16:14) The Revolut culture: Extreme ownership + execution(00:20:12) Scaling signals: When to go all in(00:24:17) Localization ≠ translation: The real nuance(00:34:38) What most teams get wrong about global content🤝 Connect with AshishLinkedin and X YoutubeDataShines GithubAshish Ranjan Jha on Floating QuestionsPublished work on AI/MLFight Fraud with Machine Learning  [author]🤝 Connect with AhmedLinkedin and XInstagram and TikTokwww.ahmedahres.com⁠ Machine learning engineer: the job of a problem-solverAhmed Ares on Platform Rules Podcast➡️ Nativhttps://www.usenativ.com/ https://speedrun.a16z.com/companies/nativ 

AI Agents Are Quietly Turning Your Back Office Into a Growth EngineMost companies still treat finance and operations as cost centers. Sumant Shringari thinks that’s outdated — and dangerous.In this episode, Sumant explains how his company Pontus uses AI agents, automation, and machine learning to turn back-office chaos into serious business value. Finance isn’t just about reporting anymore — it’s becoming a core growth engine. By eliminating inefficiencies and unlocking operational leverage, companies are converting bloat into scalable revenue. Forget “support functions” — this is where your next margin expansion lives. If you're not deploying AI to turn operational chaos into value, you’re already falling behind.Sumant Shringari is the founder of PONTUS, where he helps the world’s largest enterprises eliminate inefficiency through outcome based automation. Before launching PONTUS, he led product at Gordian Software, powering revenue gains for travel giants like Booking.com and Priceline. A former AI researcher, Sumant has published several papers at the intersection of artificial intelligence and biology. He holds five degrees from the University of Pennsylvania—which, in hindsight, may have been excessive.#AI #AIagents #Automation #Finance #OperationalEfficiency #CostCenters #ValueDrivers #BusinessTransformation #TechnologyAdoption #MachineLearningConnect with Sumant ShringariPublished Academic Work from Sumant ShringariConnect with PONTUS🔗 LinksPontus Business Case FrameworkLenny’s Podcast + NewsletterWes Kao's NewsletterPattern Recognition and Machine Learning (Information Science and Statistics) by Christopher BishopAndrej KarpathySelling With: The art of selling with champions to shape internal buying conversations & close enterprise deals by Nate Nasralla Enjoyed the episode? Drop us a ⭐️⭐️⭐️⭐️⭐️ — it helps more builders find us. 📺 Watch on YouTube 🔥 Subscribe for weekly drops 📲 Follow us on socials here 🎙️ The AI-First Business Podcast AI isn’t coming—it’s already built. This is the podcast where you meet the ones who did it first. No panel chatter, no posturing—just sharp strategy, fast builds, and the people reshaping products, ops, and industries with AI. Disclaimer: Views are personal and do not reflect any affiliated organizations. Content is for informational purposes only.

What do venture capital, layer cakes, and early-stage startups have in common? Tyler from Menlo Ventures breaks it all down. With 15 years in the VC game, Tyler’s focus on FinTech and vertical SaaS has led him to develop the “layer cake” approach—building businesses with stacked, sustainable revenue models. He dives into the importance of long-term customer retention, adding products post-acquisition, and spotting early product-market fit.But is the layer cake model the best strategy? While Tyler champions sustainable, customer-focused growth, not everyone’s convinced. Are traditional growth models better at managing risks like customer dependency and market volatility? Tune in to hear Tyler’s take on startups, Menlo’s hands-on support for founders, and why he thinks "flat revenue cohorts" are the key to long-term success.If you’ve ever wondered how venture capitalists spot the next big thing—or how "beautiful layer cakes" fit into all of this—this episode has you covered.Topics Covered:The "layer cake" business model for scaling startups.FinTech and vertical SaaS investment strategies.Menlo Ventures' hands-on approach to building great teams.For: Founders, aspiring VCs, and anyone curious about how startups grow—or fail.Connect with Tyler Sosin hereTyler Sosin LinkedinMenlo VenturesConnect with Ash Akhan hereAsh Akhan LinkedinEpisode References:GoCardless Y CombinatorEnjoyed the episode? Drop us a ⭐️⭐️⭐️⭐️⭐️ — it helps more builders find us. 📺 Watch on YouTube 🔥 Subscribe for weekly drops 📲 Follow us on socials here 🎙️ The AI-First Business Podcast AI isn’t coming—it’s already built. This is the podcast where you meet the ones who did it first. No panel chatter, no posturing—just sharp strategy, fast builds, and the people reshaping products, ops, and industries with AI. Disclaimer: Views are personal and do not reflect any affiliated organizations. Content is for informational purposes only.

AI wasn’t always a welcome guest in cybersecurity—but that changed with Darktrace.🎙️ In this episode, CISO Michael Beck shares how he left the UK  government to tackle a new kind of threat landscape: one where traditional defenses  were no longer enough, and AI had to prove its worth in the face of deep industry skepticism .📌We break down:🔍 The early resistance: Why AI in cybersecurity was dismissed—and what it took to gain trust 📈 Darktrace’s blueprint: How anomaly detection and continuous learning  redefined cyber defense ⚙️ Adoption lessons: What teams need to know when integrating AI into legacy systems 🧍‍♂️ The human factor: Why perception, communication, and credibility are as important as the tech itself  This episode is for:👩‍💼 Leaders navigating AI integration🛠️ Builders scaling in critical industries🧠 Anyone curious how trust, timing, and innovation collide in high-stakes environments Connect with Michael Beck:LinkedinPodcase Episode: Gen AI in Cybersecurity: Innovations, Threats, and Defence Strategies with DarktraceDarktrace:WebsiteThe AI Arsenal: Understanding the Tools Shaping CybersecuritReferences:National Cyber Security Center (NCSC)Verizon Data Breach Investigations Report (2025)Red Team / Blue TeamBook Reco: Financial IntelligenceMovie Reco: WarGamesMITRE ATT&CK🎯 Did we hit the mark for you? Help us win favor with the algorithm gods, drop a 5 ⭐️ rating and like!📺 Watch the full episode now on Youtube🔥 Want early-access and exclusive content? Subscribe on Substack or follow our channelsEnjoyed the episode? Drop us a ⭐️⭐️⭐️⭐️⭐️ — it helps more builders find us. 📺 Watch on YouTube 🔥 Subscribe for weekly drops 📲 Follow us on socials here 🎙️ The AI-First Business Podcast AI isn’t coming—it’s already built. This is the podcast where you meet the ones who did it first. No panel chatter, no posturing—just sharp strategy, fast builds, and the people reshaping products, ops, and industries with AI. Disclaimer: Views are personal and do not reflect any affiliated organizations. Content is for informational purposes only.