DataFramed

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?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 LabsNew 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

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?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 ArtifexAINew to DataCamp? Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

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?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)New to DataCamp?Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

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?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 TransformationNew to DataCamp?Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

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?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 WorkRelated Episodes:The Data Team's Agentic Future, with Ketan Karkhanis, CEO at ThoughtSpotTowards Self-Service Data Engineering with Taylor Brown, Co-Founder and COO at FivetranNew to DataCamp?Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business