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DataCamp

DataFramed

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?

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
RampStripe • Fin (Intercom’s AI agent), recently announced to be acquired by Salesforce
Anthropic / Claude CodeConnect with ToddAI-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

#371 The Real Reason Your Product Team Needs a Feedback Loop with Todd Olson, CEO at Pendo

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?

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: https://www.linkedin.com/in/drdewett/

• AI-Native Course: Intro to AI for Work

• Related Episode: How to Have a Machine Learning Career in 2026 with Marina Wyss

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Learn on the go using the DataCamp mobile app

#370 Failure is Data (and Other Career Advice) | Todd Dewett, Leadership Author & Speaker

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?

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

#369 How to Become a Top Business Intelligence Analyst | Helen Wall, Founder at Helen Data Design & Microsoft Influencer

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?

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

• Lakebase: https://www.databricks.com/product/lakebase

• Apache Spark: https://spark.apache.org

• AI-Native Course: Intro to AI for Work - https://www.datacamp.com/courses/introduction-to-ai-for-work

• Related Episode: AI's Impact on Databases - https://www.datacamp.com/podcast/ais-impact-on-databases

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

#368 AI Agents Are Now Your Database's Main User | Reynold Xin, Co-Founder at Databricks

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?

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: How to Build AI Your Users Can Trust with David Colwell, VP of AI & ML at Tricentis - https://www.datacamp.com/podcast/how-to-build-ai-your-users-can-trust

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

#367 Don't Build on Jell-O: How to Make Agentic AI Reliable with Dan Klein, CTO at Scaled Cognition

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?

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 2 / Nobel Prize 2024

• Connect with James

• AI-Native Course: Intro to AI for Work

• Related Episode: #358 How AI Agents Will Work While You Sleep | Ruslan Salakhutdinov

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

#366 Can AI Agents Outperform a Data Scientist? | James Zou, Professor at Stanford University

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?

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 SuccessDr. Katia Walsh (co-author)ModernaKonectaIKEAAndrej 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

Empower your business with world-class data and AI skills with DataCamp for business - https://www.datacamp.com/business

#365 Your 90 Day Blueprint for AI Success with Charlene Li, Author of Winning with AI

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?

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 world-class data and AI skills with DataCamp for business - 
https://www.datacamp.com/business

#364 How to Enable Agentic Commerce with Nell Thomas, VP of Data at Shopify

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.

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?

 • Learn on the go using the
 DataCamp mobile app • .
 • Empower your business with world-class data and AI skills with
 DataCamp for business • .

#363 Build Your Personal Brand at Work | Dorie Clark, Executive Education Faculty at Columbia Business School

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?

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: LinkedIn

• AI-Native Course: Intro to AI for Work

• Related Episode: How to Have a Career in Data Science in 2025 with Dawn Choo

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

#369 How to Become a Top Business Intelligence Analyst | Helen Wall, Founder at Helen Data Design & Microsoft Influencer

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