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300 afleveringen, van nieuw naar oud
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#384 Is the Cloud Over? | Sergio Gago, CTO at Cloudera
26 oktober 2026 · 54 min
AI agents are only as useful as the data they can reach, and most company data is scattered across clouds, data centers, and legacy systems. Giving agents access is easy to get wrong, especially when the data includes salaries, customer records, or financial results. That's why governance, permissions, and audit trails are becoming the main bottleneck for getting agents into production. How do you give an agent the right access without exposing sensitive information? Who owns that job? And do traditional ideas like data fabrics and data meshes still hold up? Lees meer
Sergio Gago is the CTO at Cloudera. He previously held senior roles at Moody's Analytics, most recently as managing director of AI/ML and quantum, and was CTO of Rakuten's Spanish operations and of digital publishing company Zinio.
In the episode, Richie and Sergio explore hybrid infrastructure for AI, choosing between cloud, neo clouds and on-premise, data fabrics and governance for agents, why agents need data access, the changing role of data teams, ephemeral dashboards, skills for engineers, sovereign AI, and much more.
Links Mentioned in the Show:
• Cloudera: The Great AI Rearchitecture report
• Thoughtworks: Core principles of data mesh
• Cloudera Unified Data Fabric
• Apache Ranger
• OpenClaw explained
• Trino
• Connect with Sergio
• AI-Native Course: Intro to AI for Work
• Related Episode: AI Agents Are the New Shadow IT (And Your Governance Isn't Ready) with Stijn Christiaens, Chief Data Citizen at Collibra
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#383 How to Coordinate Intelligence Across Teams & Agents | Vijoy Pandey, SVP of Outshift at Cisco
19 oktober 2026 · 1 uur 5 min
AI agents are getting easier to build and connect. Open standards now let them discover each other, pass messages and use shared tools. But connecting agents is not the same as getting them to work toward the same goal, and research on multi-agent systems shows that misalignment grows with every agent you add. For professionals deploying agents, this shows up as handoffs that fail and results that don’t add up, even when each agent is right on its own. How do agents agree on what a task means? What does a team need in place before adding more agents? And how do you know the combined result is better? Lees meer
Vijoy Pandey is SVP and GM of Outshift by Cisco, the company’s incubation engine for emerging technologies such as agentic AI and quantum networking. He has spent about three decades building distributed infrastructure at Google, IBM and startups, holds a PhD in Computer Science from UC Davis, and has filed more than 80 patents.
In the episode, Richie and Vijoy explore the cognitive revolution and what it means for agents, the Internet of Cognition, coordination failures in multi-agent systems, agent protocols and guardrails, task-based access control, tiny teams with tokens (T3), organizational memory and context leaders, measuring whether agents are actually better, emergent collective intelligence, and much more.
Links Mentioned in the Show:
AGNTCY: open-source agent connectivity project
Model Context Protocol (MCP) and Agent2Agent protocol (A2A)
Outshift: Internet of Cognition
Coherent Cognition, Vijoy’s Substack
Michael Tomasello: A Natural History of Human Thinking
Michael Levin: talks and interviews
Connect with Vijoy
AI-Native Course: Intro to AI for Work
Related Episode: AI Agents Are the New Shadow IT (And Your Governance Isn’t Ready) with Stijn Christiaens, CEO at Collibra
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#382 What DataCamp's Learner Data Says About Learning | Pieter Moors, Lead Data Scientist at DataCamp
15 oktober 2026 · 32 min
Career goals in data have shifted quickly. Data scientist was long the default target, but AI engineer has appeared from almost nowhere, and companies are moving training toward AI literacy, agents, and prompt engineering. Certifications are becoming a common way to prove these skills, while many people wonder whether learning to code still matters when AI can write it for them. What are people choosing to learn right now? Which fundamentals still hold up? How do you prepare for a certification exam? And is the fear of AI replacing technical roles showing up in what people actually study? Lees meer
Pieter Moors is the Lead Data Scientist at DataCamp, where he works with the company's learner data to understand how people learn and how that behavior changes over time. He is part of DataCamp's new Data Insights program, which shares findings from that data with the world every couple of weeks.
In the episode, Richie and Pieter explore what DataCamp's own learner data reveals, when enterprise and individual learners study, how the World Cup disrupted study time, why short regular sessions and streaks build habits, how AI tutors relate to certification pass rates, the shift from data scientist to AI engineer, and much more.
Links Mentioned in the Show:
Connect with Pieter
DataCamp AI Tutor
DataCamp Certifications
Introduction to Python course
DataCamp AI Assistant
DataCamp mobile app
DataCamp subreddit
AI-Native Course: Intro to AI for Work
Related Episode: The Best Moments from RADAR 11x: AI, Jobs, Agents and Skills
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#381 Escaping the AI Hiring Doom Loop with Daniel Chait, CEO & Ophir Samson, Head of Voice AI at Greenhouse
12 oktober 2026 · 51 min
Most hiring decisions still come down to gut feel, and gut feel is full of blind spots — a favorite school, a firm handshake, the type of coffee someone carries into the room. As AI reshapes recruiting, the industry is being forced to ask whether decisions can be made on evidence instead of instinct. That means defining what actually predicts success in a role, testing every candidate against the same criteria, and being able to show the reasoning behind a hiring call rather than just a hunch. For anyone building a team, the question isn't whether bias exists — it's whether your process is built to catch it before it shapes who gets hired. Lees meer
Daniel Chait is the co-founder and CEO of Greenhouse, the hiring software used by tens of thousands of companies to run structured, data-driven hiring processes. Ophir Samson is Greenhouse's Head of Voice AI, leading the team following Greenhouse's acquisition of Ezra AI Labs, the voice AI interviewing company he founded.
In the episode, Richie, Daniel, and Ophir explore the flood of AI-generated resumes drowning both job seekers and recruiters, how Voice AI interviews let candidates showcase themselves beyond a resume, structured hiring and scorecards for fairer decisions, bias audits and anti-cheat safeguards, EU AI Act compliance, and measuring success through pass-through rates and time savings, and much more.
Links Mentioned in the Show:
• Zapier <> Greenhouse: Conversational Application Voice AI
• Paradigm Strategy
• Anthropic
• Pipecat on GitHub
• Daily
• Are Emily and Greg More Employable Than Lakisha and Jamal? (AEA)
• Connect with Daniel: LinkedIn
• Connect with Ophir: LinkedIn
• AI-Native Course: Intro to AI for Work
• Related Episode: How to Have a Machine Learning Career in 2026
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#380 The Best Moments from RADAR 11x: AI, Jobs, Agents and Skills
7 oktober 2026 · 48 min
A job title can stay the same while the work underneath it changes completely. That idea ran through RADAR 11x, our online conference, and this recap keeps the best moments from four of the day's eight sessions. You will hear how to sort your tasks between you and AI, which durable skills hold their value, why pilots stall before production, and how software teams work with coding agents. Missed RADAR? Start here. Which tasks should AI take over? Which skills deserve your next hour of learning? Lees meer
Featured in this recap: Ben Zweig, CEO at Revelio Labs; Nicole Immorlica, Professor at Yale University and Senior Principal Researcher at Microsoft; Margaret Beier, Professor at Rice University; Matt Jones, EVP of Strategy at Cielo Talent; Krishnan Hariharan, CTO and VP of Engineering for Forge at Honeywell; Satesh Sonti, Principal Specialist Solutions Architect at AWS; Laurent Gil, President and co-founder at Cast AI; Joao Moura, CEO at CrewAI; Ali Arsanjani, Senior Director of Applied AI Engineering at Google; and DataCamp co-founders Jonathan Cornelissen (CEO) and Martijn Theuwissen (COO).
In the episode, Richie rounds up the best moments from RADAR 11x, exploring whether AI will take your job, the durable skills worth building, why AI pilots stall, cutting token costs, how software teams work with agents, the future of the data analyst role, and much more.
Links Mentioned in the Show:
• We Are Not Machines (Sarah O'Connor)
• Jevons paradox
• AI-Driven Development Life Cycle (AI-DLC) (AWS)
• The State of AI Careers 2026 (DataCamp)
• Watch on demand: Humans On The Loop
• Watch on demand: Don't Waste Your Time on AI Pilots
• Watch on demand: The New Developer Workflow
• Watch on demand: Closing and AMA
• RADAR 11x event page
• AI-Native Course: Intro to AI for Work
• Related Episode: Data-Driven Workforce Analytics with Ben Zweig, CEO at Revelio Labs
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#379 The Secrets of Deploying AI in Production | Sumti Jairath, Chief Architect at SambaNova
28 september 2026 · 48 min
AI agents are moving from demos into production, but many teams run into the same wall: responses that take too long and cost too much. As models grow bigger and reason through more steps before answering, every chained agent adds delay, and that adds up fast at scale. For data and AI professionals, this raises a practical question: how do you pick a model and an inference platform that keeps a multi-agent workflow fast without blowing the budget? And underneath that choice sits a bigger one — how much of the AI stack, from chips to software, should a company actually control? Lees meer
Sumti Jairath is Chief Architect at SambaNova Systems, where he's focused on machine learning, big data analytics, and software-defined hardware since 2017. Before that, he spent close to eight years as a Senior Hardware Architect at Oracle working on hardware acceleration for machine learning and data analytics, and earlier held processor and server design roles at Hewlett Packard and Sun Microsystems. He holds a B.Tech in Electronics and Communication Engineering from the National Institute of Technology, Kurukshetra.
In the episode, Richie and Sumti explore why AI agents are slow and expensive, the architecture behind faster inference, choosing the right model and platform for a task, open-source models and sovereign AI, the full AI infrastructure stack, power efficiency, the AI data center debate, career paths in AI infrastructure, and much more.
Links Mentioned in the Show:
• NVIDIA's Groq acquisition, announced around GTC
• Chris Olah, Anthropic
• Anthropic and OpenAI's coding-agent-driven revenue growth
• Ollama
• Apple Mac mini
• SambaNova careers
• Connect with Sumti
• AI-Native Course: Intro to AI for Work
• Related Episode: From City Sewers to Sovereign AI with Russ Wilcox, CEO at ArtifexAI
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#378 The Data Engine for AI with Ledion Bitincka, CTO at Cribl & Nikhil Mungel, Head of AI R&D at Cribl
21 september 2026 · 51 min
As agents take over more of the actual coding, the nature of technical work is shifting from producing software to judging it. Engineers increasingly spend their time specifying what should be built and then checking whether an agent's output actually solved the problem, rather than writing every line themselves. That changes what skills matter for a career in data and software: problem-solving and evaluation start to outweigh knowing today's specific tools or syntax. It also raises a harder question for teams — if agents can generate work this fast, how do you know which of it is actually worth shipping? Lees meer
Ledion Bitincka is Co-Founder and CTO of Cribl, where he leads the engineering organization with a first-principles approach to product delivery. Before Cribl, he was an Advanced Development Architect at Splunk, where he worked on Search-Time Schema, Hunk, and SmartStore, and before that founded Triangulus Communications. Nikhil Mungel is Head of AI R&D at Cribl, based in San Francisco, with over 15 years building distributed systems and AI teams at companies including Substack, Splunk, and ThoughtWorks — he now leads teams building LLM-powered systems for IT and security data.
In the episode, Richie, Ledion, and Nikhil explore AI agent disasters and cost shocks, software telemetry fundamentals, using agents to analyze telemetry, AI-powered software factories, the shift from knowledge work to judgment work, skills for the agentic era, avoiding runaway AI spend, and measuring product value through growth metrics, and much more.
Links Mentioned in the Show:
• Jocko Willink's book, Leadership Strategy and Tactics: A Field Manual
• Cribl
• Cribl AI (Copilot)
• Claude Code
• AWS Graviton
• NVIDIA
• Connect with Ledion: LinkedIn
• Connect with Nikhil: LinkedIn
• AI-Native Course: Intro to AI for Work
• Related Episode: AI Agents at Work: What Actually Breaks (and How to Fix It)
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#377 The Algorithm for Hypergrowth with Jon McNeill, CEO at DVx Ventures & Former President at Tesla
14 september 2026 · 38 min
Hypergrowth companies rarely scale on strategy alone — they scale on how fast decisions get made and how much risk employees feel safe taking. A recurring idea in high-growth environments is treating decisions differently depending on whether they're reversible, and rewarding people for making an impact rather than simply avoiding mistakes. For managers and individual contributors alike, that shift changes what "good work" looks like day to day. Which of your team's decisions actually need a leader's sign-off, and which ones would move faster if people just tried something and adjusted? Lees meer
Jon McNeill is CEO and co-founder of DVx Ventures, a venture studio that has launched 12 companies. He previously served as President at Tesla, where revenue grew from $2B to $20B in 30 months, and as COO at Lyft through its IPO. A serial entrepreneur, he's founded and sold six companies, sits on the boards of Lululemon and Asurion, and wrote The Algorithm: The Hypergrowth Formula that Transformed Tesla, Lululemon, General Motors and SpaceX.
In the episode, Richie and Jon explore the algorithm behind Tesla's 10X hypergrowth, why automation should always come last, how to find and delete unnecessary process steps, building a culture of curiosity and urgency, one-way vs. two-way door decisions, small-team organizational design, changing the currency of promotion, and running effective meetings, and much more.
Links Mentioned in the Show:
• The Algorithm: The Hypergrowth Formula that Transformed Tesla, Lululemon, General Motors and SpaceX by Jon McNeill
• Incorruptible by Eric Ries
• Unreasonable Hospitality by Will Guidara
• Eleven Madison Park
• Jensen Huang on LinkedIn
• Karim Bousta on LinkedIn
• Connect with Jon
• AI-Native Course: Intro to AI for Work
• Related Episode: How to Thrive in a World of Continuous Transformation
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#376 Rethinking the Data Stack in the age of AI with Tristan Handy, President of Fivetran + dbt Labs
7 september 2026 · 42 min
Across the data and AI industry, infrastructure that once served dashboards and human analysts is being rebuilt to serve autonomous agents instead. That shift changes what "AI-ready data" actually means, pushing teams to rethink documentation, governance, and the semantic layer so agents pull consistent, trusted definitions rather than guessing. Day to day, this shows up as pressure to clean up gold-layer tables, eliminate duplicate metrics, and formalize business logic that used to live only in someone's head. It raises real questions: how clean does data need to be before agents can safely act on it, and who ends up owning that definition? Lees meer
Tristan Handy is President and Co-Founder of Fivetran + dbt Labs, the company formed by the June 2026 merger of Fivetran and dbt Labs. He founded dbt Labs in 2016 (originally as Fishtown Analytics) and spent a decade as its CEO before leading the company through the merger, and has worked in data for 23 years.
In the episode, Richie and Tristan explore the dbt and Fivetran merger, building an open and modular data stack, using data to power trustworthy AI agents, the growing importance of semantic layers, how data team structures are evolving, career advice for data practitioners, context engineering for AI-driven research, and much more.
Links Mentioned in the Show:
• Simon Willison's blog
• dbt MCP server
• Apache Iceberg
• Apache Polaris
• LookML / Looker's semantic layer
• The Vaccine Education Center (CHOP)
• Connect with Tristan
• AI-Native Course: Intro to AI for Work
Related Episodes:
The Data Team's Agentic Future, with Ketan Karkhanis, CEO at ThoughtSpot
Towards Self-Service Data Engineering with Taylor Brown, Co-Founder and COO at Fivetran
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#375 Is Math The Key to Better Coding AI? With Tudor Achim, CEO at Harmonic
31 augustus 2026 · 47 min
AI capability in mathematics jumped before most people noticed, tackling Olympiad-level problems and unsolved research questions that had resisted attack for years. But the pattern of where AI succeeds and where it stalls is uneven and worth understanding. It's much better at grinding through cases to disprove something than at constructing an elegant, original proof. Anyone working with AI in a technical field runs into this same asymmetry. Where exactly is the boundary between tasks AI can already do reliably and ones that still need human judgment and creativity? Lees meer
Tudor Achim is the co-founder and CEO of Harmonic, an AI company building toward mathematical superintelligence. He previously led the machine learning team at Quora and co-founded and served as CTO of the autonomous driving company Helm.ai. Under Tudor, Harmonic's Aristotle system achieved gold-medal performance at the 2025 International Math Olympiad alongside systems from OpenAI and Google DeepMind — with every proof formally verified.
In the episode, Richie and Tudor explore why AI is starting to outperform humans at advanced mathematics, the shift toward formally verified proofs using the Lean language, where AI already beats humans (finding counterexamples) versus where it still falls short (building elegant proofs), how human mathematicians' roles will change, why math capability gains spill over into better AI reasoning generally, and much more.
Links Mentioned in the Show:
• Tudor's TED Talk: "The Path to Mathematical Superintelligence"
• Aristotle, Harmonic's reasoning system
• Harmonic
• The Erdős Problems
• American Institute of Mathematics
• Rich Sutton, "The Bitter Lesson"
• Connect with Tudor: LinkedIn
• AI-Native Course: Intro to AI for Work
• Related Episode: Why AI Agents Haven't Taken Over Knowledge Work Yet, with Jennifer Smith, CEO of Scribe (exact URL pending — episode published Aug 17, 2026, too recent to be indexed yet; confirm link on datacamp.com/podcast before publ…
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#374 How to Thrive in a World of Continuous Transformation | Phil Le Brun and Jana Werner, Executives in Residence at AWS
24 augustus 2026 · 46 min
Technology is now moving faster than the organizations trying to adopt it. A model can be tested in an afternoon, but the approval to test it can take half a year, and that gap is where most transformation budgets quietly disappear. The structures that made companies safe and predictable — layers of sign-off, centralized control, standardized processes — were built for a world where getting things wrong was expensive. That world is gone. So what actually has to change inside a company for AI to deliver value? Which habits are holding things up? And where do you start when everything needs fixing at once? Lees meer
Phil Le Brun is an Executive in Residence at AWS and previously spent over 25 years at McDonald's Corporation, where he was VP of Global Technology Development and International CIO. Jana Werner is an Executive in Residence at AWS, where she leads the Financial Services Practice in EMEA and advises Fortune 500 executive teams on transformation, having previously scaled a tech start-up to acquisition by HP, led digital transformation at Tesco Bank, and advised DHL on global change. Together they are the authors of The Octopus Organization: A Guide to Thriving in a World of Continuous Transformation (Harvard Business Review Press).
In the episode, Richie, Phil and Jana explore why AI transformations stall, the Tin Man organization and its anti-patterns, the octopus as a model for adaptive companies, why AI adoption metrics mislead, being data informed rather than data driven, making fast reversible decisions, hiring and onboarding, embedding learning into daily work, and much more.
Links Mentioned in the Show:
• The Octopus Organization (book)
• Through the Looking-Glass — Lewis Carroll (the Red Queen)
• Goodhart's law
• Annie Duke on "resulting"
• Linda Hill, Harvard Business School
• A Seat at the Table — Mark Schwartz
• Connect with Phil
• Connect with Jana
• AI-Native Course: Intro to AI for Work
• Related Episode: Your 90 Day Blueprint for AI Succes…
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#373 What Do Your Colleagues Do All Day? (The Value of Institutional Knowledge & AI for Process Reengineering) | Jennifer Smith, CEO at Scribe
17 augustus 2026 · 52 min
Four years into the AI boom, headlines still promise agents that will run entire departments, yet most companies can't point to the transformation they were sold. The gap isn't intelligence — today's models are remarkably capable — it's context: no model arrives knowing how your company actually gets things done. For anyone tasked with deploying AI at work, this raises pressing questions. What does it take to turn generic intelligence into something that understands your specific operations? And why do so many well-funded AI initiatives stall before they ever reach production? Lees meer
Jennifer Smith is Co-Founder and CEO of Scribe, the Workflow AI platform used by more than 6 million people and 94% of the Fortune 500. Under her leadership, Scribe has surpassed $100M in ARR and raised $75M at a $1.3B valuation. Before founding Scribe, Jennifer spent three years at Greylock Partners interviewing 1,200 C-suite executives about the problems they were trying to solve, and previously worked at Coatue Management and McKinsey & Company. She holds an MBA from Harvard and a BA from Princeton.
In the episode, Richie and Jennifer explore why AI agents haven't taken over knowledge work yet, harnessing institutional knowledge as "specialized intelligence," mapping enterprise workflows with LLMs, building the business case and ROI for AI transformation, balancing top-down and bottom-up change management, and the agency-driven skills that matter most in an AI-native workplace, and much more.
Links Mentioned in the Show:
Scribe (Jennifer's company)
McKinsey & Company
Aaron Levie, CEO of Box, followed by Jennifer on X
Jaya Gupta, Partner at Foundation Capital
Connect with Jennifer: LinkedIn
AI-Native Course: Intro to AI for Work
Related Episode: AI Agents at Work: What Actually Breaks (and How to Fix It) with Danielle Crop, EVP at WNS
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#372 Bulletproof Large Scale Data Science with Srini Raghavan, Chief Product Officer at Freshworks
10 augustus 2026 · 49 min
Software buying decisions used to be made once, by someone far removed from the people actually using the tool. That model is breaking down. Teams now expect software to work out of the box, without weeks of setup, integration, and configuration before anyone sees value. At the same time, a growing share of "users" aren't people at all — they're AI agents calling the same systems through APIs and chat interfaces. That raises a set of questions worth sitting with: what happens to user experience when the user isn't human? Can personalization and simplicity coexist, or is one always traded for the other? And as building software gets cheaper, what actually separates a good product from a cluttered one? Lees meer
Srini Raghavan is Chief Product Officer at Freshworks, where he leads product strategy for the company's AI-powered customer and employee experience software. He previously served as Chief Product Officer at RingCentral and SVP of Product at Five9, and holds an MBA from the University of Chicago Booth School of Business.
In the episode, Richie and Srini explore the SaaS consolidation trend and why the "SaaSpocalypse" prediction missed the point, building software that works for both humans and AI agents, how MCP and modular architecture are reshaping product design, the rise of the "product builder" role replacing specialized titles, customer feedback loops and cohort-based A/B testing, judgment as the most important AI-era career skill, and much more.
Links Mentioned in the Show:
• Fresh Service — https://www.freshworks.com/freshservice/
• Jaya Gupta's Webinar at RADAR - https://app.datacamp.com/learn/webinars/whats-next-rethinking-analytics-for-the-ai-human-era
• Freddy AI Agent Studio — https://www.freshworks.com/freshservice/ai-agent-studio/
• Figma Make — https://www.figma.com/make/
• Cursor — https://cursor.com
• NotebookLM — https://notebooklm.google/
• Marc Andreessen's "Mexican standoff" comment — https://officechai.com/ai/programmer-product-manager-…
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#371 The Real Reason Your Product Team Needs a Feedback Loop with Todd Olson, CEO at Pendo
3 augustus 2026 · 40 min
Software teams are shipping faster than ever, but speed hasn't solved the oldest problem in the industry: most software still isn't very good. AI coding tools have lowered the barrier to building something, yet they haven't lowered the barrier to building something worth using. As more people who aren't trained software creators start shipping products, a new question is forming across product, design, and engineering teams: if AI can build almost anything, how do you make sure it builds the right thing, and builds it well? Lees meer
Todd Olson is co-founder and CEO of Pendo, the product experience platform he started in 2013. Before that, he held product and engineering roles at Rally Software, Red Hat, Cisco, and Google. He's the author of The Product-Led Organization and has led Pendo through raising over $356M in venture funding while growing to 2,300+ customers.
In the episode, Richie and Todd explore why bad software still gets built, how much context AI coding agents need before they can be trusted, using behavioral data and "rage prompts" to catch what's actually frustrating users, the shift toward headless and agentic software design, how product, design, and engineering roles are splitting apart, managing one-way-door risk during AI transformation, and much more.
Links Mentioned in the Show:
Jeff Bezos’s one-way door / two-way door decision framework: 2015 Amazon shareholder letter
HubSpot’s 2024 terms-of-service backlash
Ramp
Stripe
Fin (Intercom’s AI agent), recently announced to be acquired by Salesforce
Anthropic / Claude Code
Connect with Todd
AI-Native Course: Intro to AI for Work
Related Episode: The Data Team’s Agentic Future with Ketan Karkhanis, CEO at ThoughtSpot
New to DataCamp?
Learn on the go using the DataCamp mobile app - https://www.datacamp.com/mobile
Empower your business with world-class data and AI skills with DataCamp for business - https://www.datacamp.com/business
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#370 Failure is Data (and Other Career Advice) | Todd Dewett, Leadership Author & Speaker
27 juli 2026 · 44 min
As AI takes over more technical and routine work, the skills that set data and AI professionals apart are shifting. Raw technical ability and a high IQ still matter, but they are becoming table stakes as tools get more capable and teams get smarter. What increasingly separates people is harder to automate: communication, self-awareness, authenticity, and the ability to keep learning through failure. For anyone building a career in this space, that raises real questions. Which skills are actually worth investing in now? What holds up as AI advances? And how do you keep growing once you have already had some success? Lees meer
Dr. Todd Dewett is one of the world's most-watched leadership voices — an authenticity expert, bestselling author, and top LinkedIn Learning instructor whose courses have reached more than 25 million people across 100+ countries. After beginning his career at Andersen Consulting and Ernst & Young, he earned a PhD in organizational behavior at Texas A&M and spent a decade as an award-winning professor before going solo. He is a five-time TEDx speaker and the author of Show Your Ink.
In the episode, Richie and Todd explore why fear quietly limits careers, treating failure as data rather than a verdict, the people skills that outlast raw IQ, learnable self-awareness, authenticity at work, using AI without losing your voice, getting better at speaking and writing, building habits, escaping the success trap, and much more.
Links Mentioned in the Show:
• Todd's LinkedIn newsletter (writing + his "Creswall" comic) — https://www.linkedin.com/in/drdewett/
• Todd Dewett on LinkedIn Learning — https://www.linkedin.com/learning/instructors/todd-dewett
• Free LinkedIn Learning access via your public library — https://www.linkedin.com/learning
• Gemma Leigh Roberts, chartered psychologist — https://www.linkedin.com/in/gemmaleighroberts/
• Erin Shrimpton, chartered organisational psychologist — https://ie.linkedin.com/in/erinshrimpton
• Connect with Todd: http…
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#369 How to Become a Top Business Intelligence Analyst | Helen Wall, Founder at Helen Data Design & Microsoft Influencer
20 juli 2026 · 49 min
Business intelligence has never been only about building charts and writing queries. Most of the work that makes a report trustworthy happens below the surface — in the data models, documentation, and stakeholder conversations that users never see. For analysts, technical skill is just the starting point; understanding what the business actually needs, and why a number exists, matters just as much. So what really separates a competent analyst from a great one? How do you build something that answers the right question, not just any question? And which skills are worth investing in first? Lees meer
Helen Wall is the founder of Helen Data Design and a Microsoft-recognized business intelligence expert and LinkedIn Learning instructor. A former actuary, she has worked across financial reporting, weather data, and consulting projects, and has maintained a running list of monthly Power BI updates for close to five years. She studied math and economics at the University of Washington, and focuses on where data analytics meets design.
In the episode, Richie and Helen explore what separates a great business intelligence analyst from an average one, the iceberg model of analytics work, building and using semantic layers, taking over messy legacy projects, documenting for both humans and AI agents, how Power BI has changed over five years, keeping AI outputs consistent and cost-effective, accountability in the age of agents, and much more.
Links Mentioned in the Show:
• Connect with Helen
• Microsoft AI for Good Lab
• Power BI monthly feature updates
• SQL Server Analysis Services
• Power BI Q&A visual
• DAX (Data Analysis Expressions)
• AI-Native Course: Intro to AI for Work
• Related Episode: The Data Team's Agentic Future with Ketan Karkhanis, CEO at ThoughtSpot
New to DataCamp?
• Learn on the go using the DataCamp mobile app
• Empower your business with world-class data and AI skills with DataCamp for business
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#368 AI Agents Are Now Your Database's Main User | Reynold Xin, Co-Founder at Databricks
13 juli 2026 · 51 min
For forty years, the rule held that transactional and analytical databases had to be separate systems, connected by fragile pipelines that move data from one to the other. That assumption is now being questioned. As AI agents start generating the majority of database activity, the old architecture is being redesigned around speed, scale, and a single copy of governed data. For anyone who works with data day to day, this raises practical questions. Do you still need separate systems for live and historical data? What happens to the pipelines you maintain? And how does your stack change when agents, not people, write most of the queries? Lees meer
Reynold Xin is co-founder and Chief Architect of Databricks. He is one of the original creators of Apache Spark, where he led the design of GraphX, Project Tungsten, and Structured Streaming, co-designed DataFrames, and served as release manager for Spark 2.0. He holds a PhD in Computer Science from UC Berkeley's AMPLab and a degree in Engineering Science from the University of Toronto.
In the episode, Richie and Reynold explore self-service analytics with Genie, the ontology layer that grounds AI in enterprise data, handling hallucinations, governance and permissions for AI agents, merging transactional and analytical databases with Lakebase and LTAP, real-time analytics, controlling cost through autoscaling, the future of Spark and classic machine learning, and much more.
Links Mentioned in the Show:
• Connect with Reynold: https://www.linkedin.com/in/rxin
• Genie (Databricks data agent): https://www.databricks.com/product/genie
• Genie Ontology / Genie One: https://www.databricks.com/blog/introducing-genie-one-genie-ontology-and-genie-agents
• LTAP (Lake Transactional/Analytical Processing): https://www.databricks.com/company/newsroom/press-releases/databricks-launches-ltap-first-lake-transactionalanalytical
• Lakehouse//RT: https://www.databricks.com/blog/introducing-lakehousert-real-time-performance-unified-lakehouse
• …
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#367 Don't Build on Jell-O: How to Make Agentic AI Reliable with Dan Klein, CTO at Scaled Cognition
6 juli 2026 · 52 min
Across the AI industry, capability has exploded while trustworthiness has lagged badly behind. The same technology that writes fluent prose can invent a refund policy that was never real, and most of those errors are subtle enough that no one notices. As more teams hand high-stakes work to AI — in banking, healthcare, customer service — the cost of confident mistakes adds up fast. So how common are hallucinations, really? Can chaining models together or adding humans to the loop fix it? And is reliability something you can design into a system from the start? Lees meer
Dan Klein is the CTO and co-founder of Scaled Cognition and a professor of computer science at UC Berkeley, where he leads the Berkeley NLP Group within the Berkeley AI Research (BAIR) Lab. He previously co-founded Semantic Machines, a conversational AI company acquired by Microsoft in 2018. At Scaled Cognition he built APT (Agentic Pretrained Transformer), a frontier model designed from the ground up for reliable, policy-adherent agentic AI.
In the episode, Richie and Dan explore why AI reliability has lagged behind capability, how hallucinations hide in plain sight, the limits of humans-in-the-loop and LLM-as-judge, building reliability into model architecture, agentic systems and verifiable actions, test-driven agent development, the skills that stay valuable, digital literacy, and much more.
Links Mentioned in the Show:
• Connect with Dan: https://www.linkedin.com/in/dan-klein/
• Scaled Cognition: https://www.scaledcognition.com/
• Berkeley NLP Group: https://nlp.cs.berkeley.edu/
• Code smells (Martin Fowler): https://martinfowler.com/bliki/CodeSmell.html
• Refactoring, by Martin Fowler: https://martinfowler.com/books/refactoring.html
• "Now you have two problems" (Jamie Zawinski quote): https://regex.info/blog/2006-09-15/247
• Lean theorem prover: https://lean-lang.org/
• AI-Native Course: Intro to AI for Work - https://www.datacamp.com/courses/introduction-to-ai-for-work
• Related Episode: Ho…
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#366 Can AI Agents Outperform a Data Scientist? | James Zou, Professor at Stanford University
29 juni 2026 · 49 min
AI agents are no longer limited to automating routine tasks like customer support or report generation. Research labs and pharmaceutical companies are beginning to deploy teams of specialist AI agents capable of designing experiments, analyzing data, and proposing new hypotheses — in some cases producing results that outperform human experts. For data scientists and researchers, this raises urgent questions: Where do AI agents excel in scientific workflows today, and where do they fall short? How do you build an agent that can genuinely innovate rather than just replicate what's already been done? And what does it take to scale a single model into a fully functioning virtual research team? Lees meer
James Zou is an Associate Professor of Biomedical Data Science, and by courtesy of Computer Science and Electrical Engineering, at Stanford University. He leads the Stanford AI for Science Lab and is affiliated with Together AI. His research focuses on building AI agents for scientific discovery and data science, making AI more reliable and statistically rigorous. He has received a Sloan Fellowship, NSF CAREER Award, two Chan-Zuckerberg Investigator Awards, and faculty awards from Google, Amazon, and Adobe.
In the episode, Richie and James explore how AI scientist agents are already outperforming human experts in scientific discovery, the Virtual Lab framework for building teams of specialist AI agents that conduct real research, teaching models to innovate not just imitate through a new training paradigm called "learning to discover," DS Gym for self-improving data science agents, scaling agentic systems from a single model to a Virtual Biotech with tens of thousands of agents, Einstein Arena as the first competition platform built exclusively for AI agents, converting scientific papers into agent-native MCPs through Paper to Agent, and much more.
Links Mentioned in the Show:
• Virtual Lab (Nature paper)
• Einstein Arena
• DS Gym
• Paper2Agent
• Together AI
• AlphaFold …
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#365 Your 90 Day Blueprint for AI Success with Charlene Li, Author of Winning with AI
22 juni 2026 · 53 min
Most organizations know AI matters, but few have turned that conviction into a written plan. Ambition and hope are everywhere; a clear roadmap tied to business strategy is rare. For teams on the ground, this gap shows up as scattered initiatives, tools nobody fully uses, and a lot of activity that never adds up to real value. So where do you actually start? How do you move from a long list of use cases to a focused plan you can execute? And who in the organization should own the job of turning AI into business results? Lees meer
Charlene Li is a New York Times bestselling author and strategic advisor who has spent more than two decades helping leaders navigate disruptive change. She founded Altimeter Group, has advised 49 of the Fortune 100, and is the co-author of Winning with AI: The 90-Day Blueprint for Success (with Dr. Katia Walsh).
In the episode, Richie and Charlene explore how to get your organization AI-ready in 90 days, why you don't need a separate AI strategy, appointing an AI value owner, creating value beyond efficiency, building AI fluency, Goldilocks governance, why you should kill your AI pilots, and much more.
Links Mentioned in the Show:
Winning with AI: The 90-Day Blueprint for Success
Dr. Katia Walsh (co-author)
Moderna
Konecta
IKEA
Andrej Karpathy's LLM Wiki
Connect with Charlene: LinkedIn
AI-Native Course: Intro to AI for Work
Related Episode: Our Data Trends & Predictions for 2026 with Jonathan Cornelissen & Martijn Theuwissen
New to DataCamp? Learn on the go using the DataCamp mobile app - https://www.datacamp.com/mobile
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#364 How to Enable Agentic Commerce with Nell Thomas, VP of Data at Shopify
15 juni 2026 · 44 min
AI agents are starting to handle parts of the shopping journey that used to require human judgment — discovery, comparison, checkout. But behind every agent recommendation is a massive, invisible layer of data infrastructure. Product catalogs need to be structured, inventory synced in real time, pricing accurate, and quality signals clear. For data engineers and teams building at companies like Shopify, this shift means rethinking how data flows through systems and what "good enough" quality actually means. How do you ensure data is ready for AI? And how is this reshaping what data teams actually do? Lees meer
Nell Thomas is the VP of Data at Shopify, where she leads a team of approximately 400–500 people across data infrastructure, ML platforms, data engineering, and data science. Her career spans multiple industries including social media (Facebook), e-commerce (Etsy), politics (Hillary for America, Democratic National Committee), and now commerce. She holds an A.B. in Psychology from Harvard University and an M.A. in History & Sociology of Science from the University of Pennsylvania.
In the episode, Richie and Nell explore agentic commerce and how AI agents are transforming shopping, the role of data in enabling AI-driven commerce, Shopify's Catalog and Universal Commerce Protocol, data quality requirements for agentic systems, how the data team function is evolving at Shopify, changing skill requirements for data professionals, and Nell's unconventional career path from politics to tech.
Links Mentioned in the Show:
- Agentic Commerce on Shopify
- Universal Commerce Protocol (UCP) vs Agentic Commerce Protocol (ACP)
- Shopify Catalog Documentation
- Agentic Storefronts — Shopify Sales Channel
- ChatGPT — OpenAI's Conversational AI
- How Shopify Built Data Infrastructure at Scale
Related
Scaling Data Quality in the Age of Generative AI
New to DataCamp? Learn on the go using the DataCamp mobile app - https://www.datacamp.com/mobile
Empower your business with wo…
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#363 Build Your Personal Brand at Work | Dorie Clark, Executive Education Faculty at Columbia Business School
8 juni 2026 · 54 min
Technical skills are being commoditized faster than ever. As AI takes on more of the work that used to define a junior knowledge worker, the things that once made someone valuable are becoming table stakes. What compounds in this environment is reputation — what colleagues, clients, and decision-makers think about you when your name comes up. Lees meer
That puts new pressure on visibility. People doing great work in silence are increasingly the ones getting passed over for promotions and external opportunities. So how do you build a reputation without becoming an influencer? What does AI-era credibility actually look like? And how do you start small?
Dorie Clark teaches Executive Education at Columbia Business School and is the Wall Street Journal and USA Today bestselling author of The Long Game, Entrepreneurial You, Reinventing You, and Stand Out. She has been named four times as one of the Top 50 business thinkers in the world by Thinkers50, recognized as the #1 Communication Coach in the world by the Marshall Goldsmith Leading Global Coaches Awards, and is a frequent contributor to the Harvard Business Review.
In the episode, Richie and Dorie explore why AI fluency is the new Excel skill, tinkering with AI's jagged frontier, the security risks of agentic AI, what personal branding really means in an AI-disrupted job market, the recognized expert formula, the ladder strategy for credibility, networking with "no asks for a year," running better meetings, and much more.
Links Mentioned in the Show:
• The Jagged Frontier (HBS Working Paper)
• Agentic Misalignment: How LLMs could be insider threats (Anthropic)
• AI-powered coding tool wiped out a software company's database (Fortune)
• Reinventing You by Dorie Clark
• The Long Game by Dorie Clark
• Superteams by Ron Friedman
• Connect with Dorie on LinkedIn
• AI-Native Course: Intro to AI for Work
• Related Episode: #341 Our Data Trends & Predictions for 2026
New to DataCamp?
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#362 How to Have a Machine Learning Career in 2026 | Marina Wyss, Senior Applied Scientist at Twitch
1 juni 2026 · 48 min
The role of the machine learning engineer is being rewritten in real time. AI coding assistants are absorbing parts of the day-to-day, planning and evaluation are eating up more of the week, and the lines between machine learning engineer, AI engineer, and data scientist are blurrier than ever. For anyone working in data and AI — or trying to break in — this shift changes what skills are worth investing in, what employers actually screen for, and how interviews are run. What's still worth learning? What does a competitive portfolio look like? And how do you stand out when a thousand applicants are using bots to apply? Lees meer
Marina Wyss is a Senior Applied Scientist at Twitch (an Amazon company), where she builds production AI and machine learning systems across content understanding, recommendations, and forecasting. She came into the field from a non-traditional background — a political science undergrad and a Master's in social data science in Berlin — and has held machine learning roles at Coursera and a Berlin-based statistical consultancy along the way. Outside her day job, Marina runs a popular AI/ML YouTube channel and weekly newsletter, and coaches people transitioning into machine learning from non-traditional careers.
In this episode, Richie and Marina explore how AI is reshaping the machine learning engineer role, the shifting balance between coding and planning, why evaluation matters more than ever, the differences between ML engineer, AI engineer, and data scientist roles, how to break into the field from a non-technical background, what makes a strong portfolio project, the hiring process at big tech, how to prepare for technical interviews, networking strategies that actually work, what success looks like in your first few months on the job, and much more.
Links Mentioned in the Show
• Chip Huyen — AI Engineering (book)
• Andrew Codesmith on YouTube
• Phillip Choi on YouTube
• A Life Engineered on YouTube
• Keras
• LeetCode
• Connect with Marina…
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#361 If You Want AI to Work, Fix This Boring Thing First with Veronika Durgin, VP of Data at Saks
25 mei 2026 · 49 min
Every conversation about AI in data eventually arrives at the same question: which roles survive, and which ones get automated away? Generative AI can already draft SQL, build dashboards, and run exploratory analysis — but it still can't sit with a business stakeholder and untangle what "customer" actually means across five teams. For data professionals, that shifts the day-to-day from production work toward translation, modeling, and judgment. So which skills are worth doubling down on? Which roles are becoming central, and which are quietly disappearing? And what should anyone hiring — or being hired — be paying attention to right now? Lees meer
Veronika Durgin is the VP of Data at Saks Global, where she leads data strategy across the luxury retail group. A full-stack data executive with more than two decades of experience spanning database administration, data engineering, platform architecture, data modeling, and analytics, Veronika is a Snowflake Data Superhero and a member of CDO Magazine's Global Editorial Board. She writes about data modeling, data culture, and data leadership on her Substack and Medium.
In the episode, Richie and Veronika explore the future of data careers under AI, why analytics engineering becomes the catch-all role, the skills and hiring shifts data leaders are making, centralized data with decentralized analytics, keeping enterprise data teams agile, conceptual data modeling as the unglamorous prerequisite to AI, semantic layers, agentic commerce, and much more.
Links Mentioned in the Show:
Connect with Veronika: LinkedIn
Veronika's Substack: Think. Solve. Repeat.
dbt — referenced as the origin of "analytics engineering"
Open Data Science Conference (ODSC) — Veronika's recent talk on data and company politics
Amazon "two-way door" decisions — Bezos shareholder letter
Jessica Talisman — Veronika's recommendation for knowledge graphs and ontologies
Juan Sequeda — referenced on semantic layers and knowledge graphs
Catalog & Cocktails pod…
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#360 What's Your Biggest AI Ethical Nightmare? | Reid Blackman, CEO at Virtue Consultants
18 mei 2026 · 57 min
Most AI ethics conversations sound the same: be fair, be transparent, be accountable. The values are right, but in practice they don't get teams out of bed in the morning. Executives nod along, employees take the compliance training, and meanwhile real risks like hallucinations, cascading failures, and autonomous agents acting at scale slip through. So what shifts when teams stop chasing an ethical ideal and start naming the specific disasters they want to avoid? Who needs to be in the room to spot them? And what kind of training actually changes how people use AI day to day? Lees meer
Reid Blackman is the founder and CEO of Virtue, an AI ethical risk consultancy, and the author of The Ethical Nightmare Challenge: How to Avoid the Worst of AI (2026) and Ethical Machines (HBR Press, 2022). A former philosophy professor at Colgate with a PhD from the University of Texas at Austin, he has designed responsible AI programs for organizations including Amazon, Etsy, Kraft Heinz, Merck, US Bank, and Nationwide, and has advised the FBI, NASA, the World Economic Forum, and the Canadian government on federal AI regulations. He also hosts the Ethical Machines podcast.
In the episode, Richie and Reid explore why responsible AI fails to motivate organizations, the biggest AI ethical nightmares facing companies today, the unique risks of agentic AI including cascading failures and emergent risks, the Ethical Nightmare Challenge framework, cross-functional ENC teams, training employees in plain language, scaling AI governance, measuring success by what you avoid, and much more.
Links Mentioned in the Show:
• The Ethical Nightmare Challenge by Reid Blackman
• Ethical Machines by Reid Blackman
• Ethical Machines podcast
• Claude Code
• Connect with Reid: LinkedIn
• AI-Native Course: Intro to AI for Work
• Related Episode: #350 How to Make Hard Choices in AI with Atay Kozlovski
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#359 My Best Friend is AI with Valerie Tiberius, Professor of Philosophy at University of Minnesota
12 mei 2026 · 44 min
Valerie Tiberius is the Paul W. Frenzel Chair in Liberal Arts and Professor of Philosophy at the University of Minnesota. She is an expert in ethics, moral psychology, and well-being, and the author of five books including What Do You Want Out of Life? and the forthcoming Artificially Yours: Real Friendship in a World of Chatbots (Princeton University Press, May 2026). She previously served as President of the Central Division of the American Philosophical Association. Lees meer
In the episode, Richie and Valerie explore the purpose of friendship and whether AI can replicate it, the benefits and risks of chatbot companions for loneliness, how sycophantic AI responses distort advice and self-perception, the dangers of companion chatbots for children's social development, designing ethical AI companions that promote human flourishing, the zone of proximal development as a framework for better AI tools, and much more.
Links Mentioned in the Show:
Artificial Intimacy by Sherry Turkle
Being You: A New Science of Consciousness by Anil Seth
Liberation Day: Stories by George Saunders
Hard Fork podcast (NYT)
Connect with Valerie
AI-Native Course: Intro to AI for Work
Related Episode: #342 — "The Secrets to High AI Adoption" with Stefano Puntoni, Professor at Wharton
New to DataCamp?
Learn on the go using the DataCamp mobile app
Empower your business with world-class data and AI skills with DataCamp for business
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#358 How AI Agents Will Work While You Sleep | Ruslan Salakhutdinov, Professor at Carnegie Mellon
4 mei 2026 · 58 min
Almost every AI agent demo lands in roughly the same place: it works most of the time, looks remarkable, and then fails in a way no one anticipated. Self-driving cars hit this wall a decade ago, and agents are running into it now. For data and AI teams, the question is no longer whether agents can complete a task — it's whether they can complete it reliably enough to remove the human reviewer. Which categories of work tolerate a 90% success rate? Which absolutely don't? And where should the next layer of guardrails sit? Lees meer
Ruslan Salakhutdinov is a UPMC Professor of Computer Science at Carnegie Mellon University and one of Geoffrey Hinton's former PhD students. He has previously served as Director of AI Research at Apple and VP of Research in Generative AI at Meta. His research focuses on deep learning, reasoning, and AI agents.
In the episode, Richie and Russ explore the most exciting use cases of AI agents today, long horizon tasks, the credit assignment problem, multi-agent systems, designing reliable human-in-the-loop workflows, agent safety and guardrails, embodied and physical AI, lessons from self-driving cars, the difference between academia and industry, and much more.
Links Mentioned in the Show:
• Claude Code (Anthropic)
• Yutori
• Waymo
• Apple Project Titan
• DeepSeek-V3 Technical Report
• Kimi K2 Technical Report
• Connect with Ruslan: LinkedIn
• AI-Native Course: Intro to AI for Work
• Related Episode: AI Agents at Work: What Actually Breaks (and How to Fix It) with Danielle Crop
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#357 Data-Driven Workforce Analytics with Ben Zweig, CEO at Revelio Labs
27 april 2026 · 58 min
The data field has changed shape faster than almost any other. The role that used to be a statistician became a data scientist, became an ML engineer, and is now morphing into AI engineer. Consulting firms are hiring fewer entry-level analysts and more vibe-coders who can ship AI systems to production. For data and AI professionals, this raises immediate questions. Which parts of the work are most exposed to automation, and which are not? Where should you invest your time? And which backgrounds are now producing the strongest hires, whether you are building a team or trying to join one? Lees meer
Ben Zweig is the CEO and Co-Founder of Revelio Labs, where he leads the development of a universal HR database built on over a billion public employment profiles and more than 5 billion job postings. He holds a PhD in Economics from the CUNY Graduate Center and teaches Data Science and The Future of Work at NYU Stern. Before founding Revelio Labs, he managed Workforce Analytics projects in the IBM Chief Analytics Office and worked as a data scientist at an emerging-markets hedge fund. He is the author of Job Architecture: Building a Workforce Intelligence Taxonomy.
In the episode, Richie and Ben explore why hiring is a broken two-sided market, why jobs are bundles of tasks not skills, building universal taxonomies from billions of job postings, which data careers resist AI, advice for hiring data talent, when traditional NLP beats LLMs, and much more.
Links Mentioned in the Show:
Ben's book — Job Architecture: Building a Workforce Intelligence Taxonomy
Revelio Labs
O*NET — the US government occupational taxonomy Ben critiques
Baruch Lev — The End of Accounting
Haskel & Westlake — Capitalism Without Capital
Justified Posteriors podcast (Andrey Fradkin & Seth Benzell)
Connect with Ben: LinkedIn
AI-Native Course: Intro to AI for Work
Related Episode: Our Data Trends & Predictions for 2026 with Jonathan Cornelissen & Martijn Theuwissen
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#356 The Forecast for Time Series Forecasts with Rami Krispin, Senior Manager of Data Science at Apple
20 april 2026 · 54 min
Time series data is everywhere — from inventory systems and energy grids to financial planning and product demand. As data volumes grow, the old ways of building individual forecasting models simply don't scale. How do you forecast hundreds of thousands of products without spending months on manual modeling? How do you know when to trust automation and when to step in? And what does it actually take to produce forecasts that business stakeholders will act on? Lees meer
Rami Krispin is Senior Director of Data Science and Engineering at Apple Finance, where he leads teams working at the intersection of statistical modeling, machine learning, and production forecasting. He is the author of Hands-On Time Series Analysis with R, an open-source contributor, Docker Captain, and instructor. He holds an MA in Applied Economics and an MS in Actuarial Mathematics from the University of Michigan, where he began his journey learning time series on DataCamp — before going on to build his own course there.
In the episode, Richie and Rami explore time series foundation models and the case for scaling, traditional versus modern forecasting approaches, feature engineering in the business world, backtesting and model selection, risk management in automated forecasting, communicating forecast uncertainty to stakeholders, the evolving role of data scientists as architects, and much more.
Links Mentioned in the Show:
Forecasting: Principles and Practice (Rob Hyndman)
Nixtla
skforecast
Prophet
Connect with Rami
AI-Native Course: Intro to AI for Work
Related Episode: Developing Better Predictive Models with Graph Transformers
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#355 AI's Impact on Databases with Shireesh Thota, CVP of Databases at Microsoft
13 april 2026 · 53 min
Cloud data platforms now offer hundreds of services, plus a growing menu of SQL, NoSQL, and open source options. Unified environments promise a simpler path, but the hard trade-offs—consistency versus scale, single-writer versus sharded, RPO/RTO targets—still matter. In daily work, you may be deciding between SQL Server, Postgres, and a globally distributed JSON store, while also asking AI tools to draft queries and spot issues. Should you still learn SQL if an agent can write it? How do you validate the intent, performance, and security of generated queries? And can monitoring agents actually reduce on-call pain without taking away needed control? Lees meer
Shireesh is the CVP of Databases at Microsoft. He leads product management, engineering, and cloud operations for Azure Databases as well as App Development for Microsoft Fabric. The products in his team’s portfolio include Azure SQL Database (on-prem, Hybrid and Cloud), Azure Cosmos DB, Azure PostgreSQL, and Azure MySQL.\\n\\n
Previously, as the Senior Vice President at SingleStore, Shireesh was responsible for end-to-end engineering and product vision of the company. Before moving to SingleStore, Shireesh was a founding member of Cosmos DB, where he architected, designed, and directly contributed to multiple key pieces of the services.\\n\\n
Shireesh has 20+ years of experience on large scale, big data, scale-out, relational and schema agnostic distributed systems across SQL, Azure Cosmos DB and PostgreSQL/Citus.
In the episode, Richie and Shireesh explore how AI agents are reshaping data stacks, why unified platforms like Fabric matter, how semantic models and ontologies reduce confusion in metrics, SQL and NoSQL choices on Azure, Postgres to Cosmos DB with guidance for builders, and much more.
Links Mentioned in the Show:
Microsoft Fabric
Azure Cosmos DB
What is Azure SQL Database?
Connect with Shireesh
AI-Native Course: Intro to AI for Work
Related Episode: Six Skills Data Professionals Need To Succeed wi…
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#354 Beyond BI: Decision Intelligence with Graphs with Jamie Hutton, CTO at Quantexa
6 april 2026 · 46 min
Decision intelligence is showing up across data and AI teams as companies move beyond dashboards to decisions made with context. Graphs, entity resolution, and better data products are becoming core tools as messy, siloed data meets stricter risk and compliance needs. In day-to-day work, this means linking “James,” “Jim,” and “Jamie” across systems, enriching records with third‑party sources, and pushing models where the data already lives in your lakehouse. How do you trust your customer counts? Which links in a graph matter, and which are noise? Can graph-based context reduce LLM hallucinations enough for regulated decisions with humans still in-loop. Lees meer
Jamie Hutton is the Co-founder and Chief Technology Officer of Quantexa, where he leads the company’s global research and development organization in advancing its market-leading Decision Intelligence Platform. With over two decades of experience pioneering data-driven technologies, Jamie has been at the forefront of innovations that connect and unify data at scale to solve complex real-world challenges. He is the creator of dynamic Entity Resolution, a pioneering capability that has redefined how the world’s leading organizations transform raw data into trusted, decision-ready intelligence. This innovation enables enterprises to prepare their data for AI, uncover new revenue streams, and expose hidden connections in even the most sophisticated criminal networks. By providing the foundation for accurate, explainable, and actionable insights, Jamie’s work has empowered governments, financial institutions, and global enterprises to make faster, smarter, and more confident decisions.
Prior to co-founding Quantexa, Jamie held senior technology and analytics leadership roles at SAS and Detica, where he delivered mission-critical solutions for organizations operating in some of the most complex and high-stakes environments in the world. Jamie holds a First-Class master’s degree in computer engineering and is recognized …
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#353 The Data Team's Agentic Future with Ketan Karkhanis, CEO at ThoughtSpot
30 maart 2026 · 50 min
Data and AI platforms are racing toward agentic and even autonomous analytics. But the bottleneck is rarely the model—it’s data readiness: governed metrics, clear metadata, and a semantic layer machines can read. For data engineers and analysts, this shifts work from hand-built SQL and dashboard tweaks to designing meaning and trust. If an agent can draft column descriptions, propose a model for a new business question, and build the first dashboard layout, where do you add the most value? What do you measure to prove ROI in 30 days? How do you prevent “shiny demos” from driving strategy too early. Lees meer
Ketan Karkhanis is the CEO of ThoughtSpot. Prior to joining the company in September 2024, Ketan was the Executive Vice President and General Manager of Sales Cloud at Salesforce. He returned to Salesforce in March 2022 after his time as the COO of Turvo, an emerging supply-chain collaboration platform. Before that, Ketan spent nearly a decade at Salesforce, where he led product areas in Sales, Service Cloud, Lightning Platform, and finally Analytics, wherein as the Senior Vice President & GM of Einstein Analytics, he pioneered incredible innovation, customer success, and business acceleration from launch to over $300M and a 30,000 strong user community. Prior to Salesforce, Ketan was at Cisco Systems where he led various technology initiatives and initiatives spanning Customer Advocacy, Cisco Certifications & eLearning.
In the episode, Richie and Ketan explore AI agents for analytics, why “self‑service BI” often fails, using agents to answer questions, build dashboards, and automate data modeling, how analyst and engineer roles shift toward governance and agent design, how transparency, culture, and ROI drive safe adoption, and much more.
Links Mentioned in the Show:
Thoughtspot
Thoughspot’s Spotter Agents
Connect with Ketan
AI-Native Course: Intro to AI for Work
Related Episode: AI Agents at Work: What Actually Breaks (and How to Fix It) with Danielle Crop, EVP…
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#352 AI Agents at Work: What Actually Breaks (and How to Fix It) with Danielle Crop, EVP Digital Strategy & Alliances at WNS
23 maart 2026 · 56 min
AI agents are spreading across the data and AI industry, promising to automate everything from research to outreach. At the same time, teams are learning that these tools can hallucinate, leak data, or act in surprising ways. In day-to-day work, the challenge is deciding which tasks to hand off, what data to share, and how to keep the output trustworthy. Do your agents actually add value, or just add noise? Are they running in a secured, ring-fenced environment? How do you balance playful experimentation with critical checking when an agent confidently gets a key fact wrong? Lees meer
Danielle leads go-to-market strategy at WNS, Capgemini's AI transformation services arm. Previously, Danielle was Chief Data Officer at American Express and Albertsons. She also write The Remix substack on technology trends, and is an Editorial Board Member for CDO Magazine.
In the episode, Richie and Danielle explore AI agents at work, experimentation with guardrails, data privacy, access, tone controls, OpenClaw automation wins and failures, token costs, tying AI plans to P&L strategy, shifts in careers and hiring, how data teams handle unstructured data governance, and much more.
Links Mentioned in the Show:
WNS
Connect with Danielle
AI-Native Course: Intro to AI for Work
Catch Danielle speaking at RADAR—April 1
Related Episode: AI Agents Are the New Shadow IT (And Your Governance Isn’t Ready) with Stijn Christiaens, CEO at Collibra
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#351 Will World Models Bring us AGI? with Eric Xing, President & Professor at MBZUAI
16 maart 2026 · 1 uur 4 min
World models are emerging as the next step after large language models, pushing AI from book knowledge toward systems that can simulate the physical and social world. Instead of just generating text or short videos, the goal is steerable simulation with long-horizon consistency and planning. For practitioners, this raises practical choices: what data and representations do you need, and when do you mix symbolic reasoning with generative models? How do you test whether a model can follow actions over minutes, not seconds? And where do you start—robotics, driving safety, or synthetic data generation? Lees meer
Professor Eric Xing is President of Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) and a world-leading computer scientist whose work spans statistical machine learning, distributed systems, computational biology, and healthcare AI. A fellow of AAAI, IEEE, and the American Statistical Association, he has authored over 400 research papers cited more than 44,000 times.Before MBZUAI, Eric was a Professor of Computer Science at Carnegie Mellon University, where he also founded the Center for Machine Learning and Health. He is the founder and chief scientist of Petuum Inc., recognized as a World Economic Forum Technology Pioneer, and has held visiting roles at Stanford and Facebook. He holds PhDs in both Molecular Biology and Computer Science.
In the episode, Richie and Eric explore world models as simulators for action, the jump from book intelligence to physical and social skills, why long-horizon planning is still hard, architectures, robots, data generation, open K2 Think LLMs, virtual-cell biology, and much more.
Links Mentioned in the Show:
MBZUAI
Pan World Model
Connect with Eric
AI-Native Course: Intro to AI for Work
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#350 How to Make Hard Choices in AI with Atay Kozlovski, Researcher at the University of Zurich
9 maart 2026 · 1 uur 10 min
Across the AI industry, high-stakes tools are being deployed in places where errors can harm people: sepsis alerts in hospitals, identity checks, welfare fraud detection, immigration enforcement, and recommendation systems that shape life outcomes. The pattern is familiar: scale and speed go up, while human review becomes rushed, shallow, or punished for disagreeing. In daily work, that can look like a nurse forced to act on false alarms, or a team using an LLM summary in ways the designers never planned. When should you slow down deployment? How do you detect new “wild” use cases early? And what does responsible tracking and oversight look like under real pressure? Lees meer
Atay Kozlovski is a Postdoctoral Researcher at the University of Zurich’s Center for Ethics. He holds a PhD in Philosophy from the University of Zurich, an MA in PPE from the University of Bern, and a BA from Tel Aviv University. His current research focuses on normative ethics, hard choices, and the ethics of AI.
In the episode, Richie and Atay explore why AI failures keep happening, from automation bias to opaque targeting and hiring models. They unpack “meaningful human control,” accountability, and design in healthcare, government, and warfare. You’ll also hear about deepfakes, consent, digital twins, and AI-driven civic engagement, and much more.
Links Mentioned in the Show:
“Lavender” IDF recommendation system
Amnesty International reports on AI/automation in welfare systems
“Meaningful Human Control” (MHC) framework
Connect with Atay
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#349 From AI Governance to AI Enablement with Stijn Christiaens, Chief Data Citizen at Collibra
5 maart 2026 · 53 min
Data governance has been around long enough to develop playbooks, but AI governance is evolving in real time. Industry trends like LLMs, agents, and emerging “swarms” are changing what oversight even means, from data lineage to agent-to-agent provenance. Lees meer
For working teams, the questions are immediate: who leads—legal, security, IT, data, or a new AI role? How do you set standards so engineers aren’t using a different tool for every task? What maturity framework should you measure against, and how often should you reassess as technology shifts? How do you help teams move fast without breaking trust?
Stijn is a data governance veteran and one of the leading thinkers in the space. He runs data strategy, data infrastructure, and product evangelism at the data and AI governance company Collibra. Since founding Collibra 18 years ago, Stijn has held several executive positions, including COO and CTO.
In the episode, Richie and Stijn explore AI governance failures and wins, risks from agents that can act on systems, creating visibility with an agent registry, how AI governance differs from data governance, ownership across legal, security, IT, and data teams, EU AI Act risk tiers, and much more.
Links Mentioned in the Show:
Collibra
Connect with Stijn
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#348 AI Agents in Your Systems: Speed, Security, and New Access Risks with Jeremy Epling, CPO at Vanta
2 maart 2026 · 44 min
Automation is moving from APIs to full “computer use,” where agents click through screens like a human. That power is transforming evidence collection, access reviews, and repetitive security tasks, but it also raises new risk. In everyday workflows, the safest gains often start with read-only actions, sandboxes, and clear opt-in for anything that writes changes. Do your tools know when an access request is an anomaly? Can you keep humans in the loop with fast review-and-approve steps? And if an agent can browse your systems, how do you stop data from walking out the door before customers or attackers notice? Lees meer
Jeremy Epling is Chief Product Officer at Vanta, where he leads product strategy and execution for the company’s trust management platform. He focuses on helping organizations automate security and compliance, enabling them to build and scale with confidence.
Previously, he was VP of Product at GitHub, overseeing Actions, Codespaces, npm, and Packages—core components of the modern developer workflow used by millions worldwide. Before GitHub, Jeremy spent more than 16 years at Microsoft, leading product teams across Azure DevOps Pipelines and Repos, OneDrive, Outlook, Windows, and Internet Explorer. His work has centered on developer platforms, cloud infrastructure, and productivity tools at global scale.
In the episode, Richie and Jeremy Epling explore AI-driven security risks, vendor data use and trade-secret leakage, governance and access controls, compliance beyond audits, how agents automate security questionnaires and vendor reviews, how to ship faster safely, human-in-the-loop design, and “computer use” automation, and much more.
Links Mentioned in the Show:
Vanta
Vanta State of Trust Report
Connect with Jeremy
AI-Native Course: Intro to AI for Work
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#347 Let's Get Physical with AI with Ivan Poupyrev, CEO at Archetype AI
23 februari 2026 · 46 min
Physical AI is showing up across the industry as sensors, connected devices, and foundation models move from the cloud into the real world. After years of IoT wiring everything to the internet, the big shift is turning raw measurements and video into meaning, not just dashboards. For day-to-day teams, that changes how you monitor equipment, detect failures, and decide what to do next. When thousands of sensor streams hit storage, who turns them into insights and recommendations fast enough to matter? Can one model generalize across different sensors and conditions? And what must run on the asset versus the cloud? Lees meer
Dr. Ivan Poupyrev is CEO and Founder of Archetype AI, where he is building a multimodal AI foundation model that combines real-time sensor data and natural language to help people and organizations better understand and act on the physical world. The company is developing a developer platform to unlock new applications of Physical AI across industries.
Previously, he was Director of Engineering at Google’s Advanced Technology and Projects (ATAP) division, where he founded and led large cross-functional teams to create Soli, a radar-based sensing platform, and Jacquard, a connected apparel platform powered by smart textiles and embedded ML. These technologies shipped in more than 15 products across 33 countries, including collaborations with Levi’s, YSL, Adidas, and Samsonite, and were integrated into flagship devices such as Pixel 4 and Nest products. His work has been widely published, recognized with major international awards, and featured in global media.
In the episode, Richie and Ivan explore physical AI beyond robotics, turning IoT sensor streams into insights, recommendations, and automation, why physical foundation models differ from LLMs, sensor-fusion wins like wind-turbine failure alerts, edge deployment and privacy, how to pick a first project in practice, and much more.
Links Mentioned in the Show:
Archetype AI
Attention Is All You Need…
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#346 Get Quantum Ready with Yonatan Cohen, CTO at Quantum Machines
16 februari 2026 · 49 min
Quantum computing is advancing fast, but it comes with a core industry challenge: noise. The big promise—better simulations, faster optimization, and maybe new kinds of AI—depends on quantum error correction and scaling from physical qubits to reliable logical qubits. For working professionals, that translates into system design questions, not just theory. How do you budget for the overhead of error correction? What does a hybrid quantum‑classical workflow look like when classical processors must process error data in real time? If a quantum approach shows “advantage” today, how do you know a better classical heuristic won’t catch up next month? Where should you focus first: hardware readiness or use cases? Lees meer
Dr. Yonatan Cohen is a physicist, entrepreneur, and co-founder of Quantum Machines, where he serves as Chief Technology Officer. He earned his Ph.D. at the Weizmann Institute of Science in Israel, focusing on quantum electronics, superconducting–semiconducting devices, and microfabrication. He is also a co-founder and former managing director of the Weizmann Institute’s entrepreneurship program and has published extensively in peer-reviewed journals, with recognized contributions to quantum computing. As CTO, Dr. Cohen has played a key role in developing the Quantum Orchestration Platform, a first-of-its-kind control and operating system for quantum computers that accelerates the path to practical, useful quantum systems.
In the episode, Richie and Yonatan explore near-term quantum simulation, encryption risks, the open question of quantum AI, noisy qubits and error correction, physical vs logical scaling, the need for algorithms and use cases, how to try quantum coding via Amazon Braket, and much more.
Links Mentioned in the Show:
Quantum Machines
Amazon Braket
IBM Qiskit
NVIDIA Cuda Quantum
Google Cirq
Connect with Yonatan
AI-Native Course: Intro to AI for Work
Related Episode: Developing Better Predictive Models with Graph Transformers with Jure Le…
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#345 How to Drive Innovation with Brian Solis, Head of Global Innovation at ServiceNow
9 februari 2026 · 1 uur 8 min
AI moves fast, and the news cycle can feel like a fire hose. New tools like agents and digital twins promise to help, but they also add more choices and noise. In day-to-day work, the challenge is less about knowing every breakthrough and more about deciding what matters, then making time to act. How do you cut meetings down, say no without friction, and still ship real work? How do you open your mind to new ideas while avoiding hype? And when you do spot a signal, how do you turn it into action across teams, stakeholders, and shifting priorities. Lees meer
As the Head of Global Innovation at ServiceNow, Brian Solis drives vision and strategy for future-focused innovation. He has three decades of experience as a technology leader, and Forbes called him "one of the more creative and brilliant business minds of our time". Previously, Brian was VP of Global Innovation at Salesforce. He has written nine books, including the best selling "Mindshift". Brian is an author of the ServiceNow Enterprise AI Maturity Index 2025 Report.
In the episode, Richie and Brian explore the challenges of staying updated with AI advancements, the importance of mindset shifts for innovation, the role of storytelling in driving change, and practical strategies for managing information overload, fostering organizational transformation, and much more.
Links Mentioned in the Show:
Brian’s Book: Mindshift
ServiceNow
Connect with Brian
AI-Native Course: Intro to AI for Work
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#344 Governing Pandora's Box: Managing AI Risks with Andrea Bonime-Blanc, CEO at GEC Risk Advisory
2 februari 2026 · 51 min
AI leaders talk about innovation, but the wider reality is messy: fast change, uneven guardrails, and threats that span cyber, reputation, and customer harm. Industry-wide, organizations are shifting from one-off compliance to lifecycle governance—from inception to decommissioning—supported by boards, CEOs, and frontline teams. For professionals, that shows up as coordination work: shared metrics, incentives for responsible delivery, embedded ethics partners, and rapid-response groups when a new risk appears. How do you decide who is accountable for model behavior? What signals should trigger escalation? And what sources can you trust to stay informed without getting overwhelmed? Lees meer
Andrea Bonime-Blanc, JD/PhD, is founder and CEO of GEC Risk Advisory, a board member, strategic advisor, and award-winning author. She specializes in the governance of change, advising companies, NGOs, and governments on global strategic risk, leadership trust, geopolitics, sustainability, cyber resilience, and exponential technologies. A former C-suite executive at four global companies, including Bertelsmann and PSEG, she has held roles spanning legal, risk, ethics, sustainability, and cybersecurity, and currently serves on multiple boards and advisory boards.
Andrea is a Senior Fellow at The Conference Board, NYU’s Center for Global Affairs, and an AI Ethics Strategy Fellow at the American College for Financial Services. She is a sought-after keynote speaker and media commentator, appearing in outlets such as Bloomberg, the Financial Times, and The New York Times. She is the author of several books, including Gloom to Boom and most recently, Governing Pandora: Leading in the Age of Generative AI and Exponential Technology.
In the episode, Richie and Andrea explore the rapid advancements in AI, the balance between innovation and risk, the importance of adaptive governance, the role of leadership in tech governance, and the integration of ethics in AI development, and much more.
Links…
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#343 Vibe Coding and the Rise of the Non-Developer Builder with Matt Palmer, Developer Relations at Replit
26 januari 2026 · 47 min
Data and AI teams are drowning in tools, but the big trend is consolidation and speed. AI-driven building is making dashboards, internal apps, and even data workflows feel more like products than reports. Custom interfaces, interactive presentations, and ad hoc apps are becoming easier to create than traditional BI artifacts. Lees meer
For working professionals, this raises practical questions: should you build a bespoke reporting site instead of another spreadsheet? Can you connect secure data views and prevent leaks by design? What does quality control look like when an agent writes the code—separate chats, clear plans, and tests? And what’s the real cost of going from idea to deployed app: a few dollars, or hundreds?
Matt Palmer works at the intersection of developer experience, product marketing, and AI education. Leading Developer Relations at Replit, he helped grow Replit's revenue from $5M to $100M+. He creates content on vibe-coding, data transformation, AI, and more—blending technical depth with accessibility to empower developers and make complex tools approachable.
In the episode, Richie and Matt explore the power of vibe coding, how non-developers are building impactful tools, the potential of AI in app development, the role of Replit in simplifying coding, and the future of personalized applications in data teams, and much more.
Links Mentioned in the Show:
Replit
Course: Vibe Coding with Replit
Your Guide to Replit
Connect with Matt
AI-Native Course: Intro to AI for Work
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#342 The Secrets to High AI Adoption with Stefano Puntoni, Professor at Wharton
19 januari 2026 · 52 min
AI tools are becoming part of daily work for more professionals than ever before, yet adoption rates vary significantly across functions and company sizes. What separates organizations that successfully integrate AI from those that struggle? How do psychological factors like identity and autonomy shape how workers respond to AI implementation? And what role does corporate culture play in determining whether AI becomes a source of innovation or a point of resistance? Lees meer
Stefano Puntoni is the Sebastian S. Kresge Professor of Marketing at The Wharton School. Prior to joining Penn, Stefano was a professor of marketing and head of department at the Rotterdam School of Management, Erasmus University, in the Netherlands. He holds a PhD in marketing from London Business School and a degree in Statistics and Economics from the University of Padova, in his native Italy.
His research has appeared in several leading journals, including Journal of Consumer Research, Journal of Marketing Research, Journal of Marketing, Nature Human Behavior, and Management Science. He also writes regularly for managerial outlets such as Harvard Business Review and MIT Sloan Management Review. Most of his ongoing research investigates how new technology is changing consumption and society, including how humans are adopting and evolving with AI.
He is a former MSI Young Scholar and MSI Scholar, and the winner of several grants and awards. He is currently an Associate Editor at the Journal of Consumer Research and at the Journal of Marketing. Stefano teaches in the areas of marketing strategy, new technologies, brand management, and decision making.
In the episode, Richie and Stefano explore the challenges of AI adoption in businesses, the psychological impacts on workers, the balance between human expertise and AI, the potential mental health effects of AI chatbots, and much more.
Links Mentioned in the Show:
Wharton School
Connect with Stefano
MIT Report—The GenAI Divide: State of AI in Bus…
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#341 Our Data Trends & Predictions of 2026 with DataCamp's CEO & COO, Jonathan Cornelissen & Martijn Theuwissen
15 januari 2026 · 51 min
2026 is shaping up to be a pivotal year for data, AI, and how we work. From step-change improvements in foundation models to AI-native workflows reshaping careers, commerce, and education, the pace of change shows no signs of slowing. After revisiting and scoring their previous predictions, Richie, Jo, and Martijn turn their focus to what’s coming next in 2026. Lees meer
Building on last year’s discussion, we explore how AI will transform hiring and career progression, why personal AI tutors could become the default learning experience, how AI agents may begin executing real economic activity, and whether we’re on the brink of another “GPT-3 moment” driven by new hardware and scaling.
Links Mentioned in the Show:
Blog: The Junior Hiring Crisis
Blog: The agentic commerce opportunity: How AI agents are ushering in a new era for consumers and merchants
Alex Banks on the ChatGPT era ending
Spec & Evals Driven Agent Development (SEDAD) Template
AI-Native Course: Intro to AI for Work
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#340 Reviewing Our Data Trends & Predictions of 2025 with DataCamp's CEO & COO, Jonathan Cornelissen & Martijn Theuwissen
14 januari 2026 · 39 min
2025 was another huge year for data and AI. Generative AI continued to reshape how we work and interact with technology, with organizations moving beyond experimentation and pushing AI firmly into production. We saw major progress in foundation models, the rise of long-running AI agents, production-ready generative video, and wider adoption of synthetic data. At the same time, AI literacy, adoption, and ROI became central concerns for boards and executives, not just technical teams. Lees meer
This time last year, DataCamp Co-Founders Jonathan and Martijn made a series of predictions about data and AI for 2025. Today, they join Richie to reflect on how those predictions played out—and to share their vision for where data and AI are headed next.
In the episode, Richie, Jonathan, and Martijn review the real-world adoption of generative AI, the shift from hype to production, the growing importance of AI literacy and usage at the executive level, the rise of longer-running AI agents, the near-mainstreaming of generative video, Europe’s position in the global AI race, why educators may be among the biggest AI adopters, and why AI hype continues to thrive—plus what they got right, what they got wrong, and what comes next.
Links Mentioned in the Show:
The DataCamp Data & AI Literacy Report 2025
AI-Native Course: Intro to AI for Work
Related Episode: Data Trends & Predictions 2025 with DataCamp's CEO & COO, Jonathan Cornelissen & Martijn Theuwissen
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#339 Modern Analytics with Mike Palmer, CEO at Sigma
5 januari 2026 · 45 min
Self-service analytics has been a goal for data teams for years, but recent advances in AI are accelerating progress in unexpected ways. The combination of natural language interfaces and spreadsheet-like tools is lowering barriers to data access across organizations. But how do you balance the freedom of self-service with the need for governance and accuracy? What skills do analysts need to work effectively with AI systems that don't always produce the same results twice? And when AI-generated answers might be slightly off, how do you know when to trust them? Lees meer
Mike Palmer is Chief Executive Officer of Sigma , where he leads the company’s strategy and growth as a cloud-native analytics and business intelligence platform. Since joining Sigma in 2020, he has focused on expanding access to cloud data by enabling business users to analyze data warehouses through familiar, spreadsheet-based workflows. Prior to Sigma, Mike served as Chief Product Officer at Druva, where he was part of the executive team scaling the company’s cloud data management platform and supporting rapid revenue growth. Before that, he was EVP and Chief Product Officer at Veritas Technologies, leading the transformation and modernization of a large enterprise data protection portfolio following its separation from Symantec. Earlier in his career, he held senior general management and executive roles at Seagate Technology and Verizon Enterprise Solutions, overseeing large-scale cloud, security, and enterprise infrastructure businesses. Mike is based in San Francisco and has spent his career building and operating enterprise data and analytics platforms at scale.
In the episode, Richie and Mike explore the journey towards self-service analytics, the role of AI in democratizing data access, the challenges of stochastic processes, the evolution of analytics applications, how businesses can leverage AI for personalized insights, the future of enterprise software, and much more.
Links Mentioned in the S…
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#338 The New Paradigm for Enterprise AI Governance with Blake Brannon, Chief Innovation Officer at OneTrust
29 december 2025 · 59 min
AI governance is becoming critical as organizations deploy more intelligent systems across their operations. With predictions of over a billion AI agents entering the workforce in the coming years, traditional governance approaches simply cannot keep pace. How do you ensure your AI systems are using data responsibly without slowing down innovation? What happens when an AI agent makes decisions that were never explicitly programmed? And how do you build governance processes that scale alongside rapidly expanding AI adoption while maintaining trust with customers and regulators? Lees meer
Blake Brannon is Chief Innovation Officer at OneTrust, where he leads product vision and strategic direction for the company’s AI-ready governance platform. He has been with OneTrust since 2017, previously serving as Chief Technology Officer, and has played a key role in scaling the platform to support privacy, data governance, risk, and responsible AI initiatives for large enterprises. Blake is based in Atlanta and holds an academic background from the Georgia Institute of Technology, with early research experience in network systems and wireless communications.
In the episode, Richie and Blake explore AI governance disasters, the importance of consent and data use, the rise of AI agents, the challenges of scaling governance processes, the need for continuous observability, the role of governance committees, strategies for effective AI governance in organizations, and much more.
Links Mentioned in the Show:
OneTrust
Connect with Blake
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#337 DataFramed, Distilled. The Best Moments of 2025 with Richie Cotton
22 december 2025 · 19 min
2025 was the year AI stopped being a curiosity and started reshaping real work. From data analysts speeding up entire workflows in minutes, to managers learning how to lead hybrid teams of humans and agents, the pace of change has been relentless. Across DataFramed this year, one theme kept surfacing: AI isn’t replacing data professionals—it’s raising the bar on what good looks like. Skills are shifting, careers are becoming more fluid, and organizations are being forced to rethink how they build teams, make decisions, and govern technology that now reasons, plans, and acts on our behalf. This Best of 2025 episode pulls together the most important ideas, voices, and debates from a year that fundamentally changed how data and AI show up in practice. Lees meer
In this special year-end roundup, Richie revisits the standout moments from DataFramed in 2025, spanning careers, business intelligence, data literacy, AI agents, industry use cases, and responsible AI foundations. You’ll hear why the data analyst role is evolving rather than disappearing, how hybrid human–AI teams are becoming the norm, and why communication remains the most underrated skill in data careers, the state of BI and data storytelling, the shift from training to behavior change in data and AI literacy, the rapid rise of agentic systems powered by reasoning at inference time. We also dive into real-world applications across healthcare, finance, and enterprise operations, alongside hard truths about data quality, governance, and model lineage. Finally, we spotlight advances in data science, NLP, and synthetic data—rounding out a year defined by faster cycles, higher expectations, and a renewed focus on getting the fundamentals right as AI scales.
Episodes Featured in this Recap:
#326 Is the Data Analyst Role Dying Out? with Mo Chen, Data & Analytics Manager at NatWest Group
#319 Building & Managing Human+Agent Hybrid Teams with Karen Ng, Head of Product at HubSpot
#295 How To Get Hired As A Data Or AI Engi…
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#336 From City Sewers to Sovereign AI with Russ Wilcox, CEO at ArtifexAI
15 december 2025 · 1 uur 12 min
The concept of sovereign AI is becoming increasingly critical in our interconnected world. Nations and organizations are grappling with who controls the data, infrastructure, and technology that power artificial intelligence systems. But what does this mean for your work in data science and AI implementation? How do you navigate the complex landscape of data ownership when building AI solutions? As geopolitical tensions influence technology development, understanding the nuances of AI sovereignty isn't just for governments—it's essential for anyone working with data and AI systems to ensure resilience and compliance in an uncertain future. Lees meer
Russ Wilcox is the CEO of ArtifexAI, advising organizations on technology strategy, AI governance, and policy analysis. With 16 years in machine learning and AI, he focuses on translating complex policy and emerging tech trends into actionable strategy. His work spans government, infrastructure, and enterprise, with a focus on connecting technical capabilities to real-world implementation. A two-time World Economic Forum speaker and TEDx presenter, Wilcox has advised government agencies and Fortune 500 companies on AI strategy, urban intelligence, and technology policy. He also serves on AI ethics boards, lectures at UCLA and Boston University, and develops NLP systems for public- and private-sector use. Russ provides strategic consulting and speaking on AI governance, technology competition, and sustainable infrastructure.
In the episode, Richie and Russ explore the US-China AI race, the philosophical differences in AI approaches, the concept of sovereign AI, the role of data sovereignty, and the potential for AI to transform infrastructure and governance, and much more.
Links Mentioned in the Show:
ArtifexAI
Russ’ Website
Connect with Russ
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#335 Rebuilding Trust in the Digital Age with Jimmy Wales, Founder at Wikipedia
8 december 2025 · 1 uur 3 min
The internet has transformed how we access information, but it's also created unprecedented challenges around trust and reliability. How do we build digital spaces where collaboration thrives and quality information prevails? What separates toxic online environments from productive ones? The principles of neutrality, transparency, and assuming good faith have proven essential in creating sustainable knowledge communities. But these same principles extend far beyond the digital realm—they're fundamental to effective leadership, successful business relationships, and even political discourse. When trust breaks down, everything becomes more difficult. So what practical steps can we take to foster trust in our organizations and communities? Lees meer
Jimmy Wales is an American-British internet entrepreneur best known as the founder of Wikipedia and co-founder of Fandom. Trained in finance at Auburn University and the University of Alabama, he began his career in quantitative finance before moving into early web ventures, including Bomis and the free encyclopedia project Nupedia. In 2001, he launched Wikipedia, which quickly became one of the most visited websites in the world. To support its growth, he established the Wikimedia Foundation in 2003, where he continues to serve on the Board of Trustees and act as a public spokesperson. He later co-founded Fandom in 2004, expanding the wiki model to entertainment, gaming, and niche communities. Wales has also pursued experiments in collaborative journalism, including WikiTribune and its successor WT Social. His work in open knowledge has earned recognition from organizations such as the World Economic Forum, Time magazine, UNESCO, and the Electronic Frontier Foundation. He has held fellowships and board roles at institutions including Harvard’s Berkman Center and Creative Commons.
In the episode, Richie and Jimmy explore the early challenges of Wikipedia, the importance of trust and neutrality, the role of AI in content creation, …