Imagine being able to vibe-code full-blown video games... for free! My returning guest, Dr. Andrey Kurenkov, helped engineer Astrocade to do just that... and already 20 million people have played games through their platform.
More on Andrey:
• Founding A.I. Lead at Astrocade, a Bay Area-based startup that has raised $68m in venture capital to create the TikTok of video games, where creators create games for free and you play them for free.
• Co-host (alongside Jeremie Harris) of my favorite podcast, "Last Week in A.I.".
• Holds a PhD from Stanford University, where his research focused on machine vision and robotics.
In this episode, we discuss:
• The fascinating Astrocade journey, of course.
• The surprising pace of humanoid robotics.
• Why he's a skeptic on Artificial Super Intelligence.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
Filtering by Category: Interview
TrueFoundry’s Nikunj Bajaj on How to Get $100M Returns on AI Agent Deployments
Imagine being able to deploy an AI agent and getting a return of over $100m from that single deployment. My guest today, Nikunj Bajaj, has facilitated that multiple times! Lots to learn from him, enjoy!
Nikunj:
• CEO and co-founder of TrueFoundry, a Bay Area-based startup that has raised over $20m to solve the thorniest problems that enterprises face when deploying agents.
• His clients include demanding organizations like NVIDIA and Siemens.
• Was previously ML tech lead at Facebook.
• Holds a master's in computer science from University of California, Berkeley.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
End-to-End Foundation Models for the Energy Industry, with Jazmia Henry
What does it take to build foundation LLMs from scratch today? Deeply impressive Jazmia Henry breaks down the four stages in today's episode, enjoy!
Jazmia:
• Holds degrees from Tulane University and Columbia University... and is partway through a PhD at the University of Oxford.
• Held a technical fellowship at Stanford University.
• Previously worked as a data strategist at Morgan Stanley, head of ML at The Motley Fool and a Lead Applied AI engineer at Microsoft.
• Published a top paper at NeurIPS, the world's most prestigious academic AI conference.
• Currently works as "Member of Technical Staff for AI/ML" at collide., a Texas-based startup that’s building AI infrastructure (including all aspects of specialized foundation models) for the energy industry.
Key topics covered in this episode include:
• What foundation models are.
• Her "full-stack" foundation-model building's four distinct stages.
• How reinforcement learning (RL) models are "bursty" because they idle the GPU during reward calculation and then dump enormous loads on it all at once.
• Reward hacking by RL models.
Thanks to Mark Freeman II for recommending Jazmia as a guest.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
How to Build AI-First Organizations, with Jacob Miller and Jeremy Mumford
After today's fun episode with Jacob and Jeremy — authors of the brand-new book "Architected Intelligence" — you’ll have all the key info to build successful AI features, AI products and AI-first companies. Enjoy!
Jeremy Mumford and Jacob Miller serve as Lead AI Engineer and Vice President of Platform Intelligence, respectively, at Pattern, a giant Utah-based tech company that IPO’ed on the Nasdaq exchange about six months ago.
Jacob and Jeremy's brand-new "Architected Intelligence" book was published by Wiley and this episode focuses almost exclusively on this invaluable book.
Episode highlights include:
• The "User Agnosticism Tenet", which means designing products and processes so they can be executed equally well by a human, an AI agent, or any hybrid combo.
• The shift in the "define-build-feedback" loop today where "building" is no longer the bottleneck, which means "definition" and "feedback" are where teams win or lose.
• Why workflows are deterministic, predictable, and cheaper than agents, and why the natural progression is skills first, then workflows, and only then agents.
• Why data engineering is the bedrock of AI engineering.
• Why velocity is the only durable moat in a world where everyone has access to the same frontier models.
Thanks to podcast superfan Jonathan Bown for recommending Jeremy and Jacob as guests!
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
Pair Programming with AI in Your Python Notebook, with Dr. Trevor Manz
Exceptional technical episode today with Dr. Trevor Manz on "marimo Pair", an actually!) game-changing pair-programming A.I.-agent companion that lifts heavy loads within your Python data-science notebook.
More on Trevor:
• 27-time NCAA Swimming All-American & National Champion.
• Master's in Computational Biology from University of Cambridge.
• PhD in Bioinformatics from Harvard University.
• Creator of the popular open-source "anywidget" project (amongst many others, particularly in visualizing bioinformatics data, e.g., genomics data).
• Now a founding engineer at marimo.io, where he is leading the charge on marimo Pair.
Seriously, marimo Pair is unreal. A complete reimagining of what's possible in a Jupyter notebook-style environment in the agentic A.I. era. You will hear (and see) my mind explode in this episode!
We also discuss:
• Agent skills.
• Recursive language models.
• A number of other open-source projects, largely in data viz/analysis.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
Security for Mythos-Era Agentic Risks, with Rubrik’s Anneka Gupta and Cal Al-Dhubaib
Mythos finds security vulnerabilities at ~100X the rate of publicly available models, and comparable open-weight models are ~6 months away. Scary? Thankfully my guests today, Anneka and Cal, have solutions!
Anneka:
• Chief Product Officer at Rubrik.
• Lecturer in Product Management at Stanford University.
• Climbed the ladder from software engineer to President (!!) during an 11-year tenure at LiveRamp.
• Holds a degree in math and computational sciences from Stanford.
Cal:
• Principal Technologist at Rubrik.
• Formerly founder and CEO of Pandata, which was acquired by Further.
• Highly sought-after keynote speaker.
• Holds a degree in data science from Case Western Reserve University.
This is an exceptional episode with two brilliant, entertaining and highly knowledgeable guests. It can be enjoyed by anyone! In it, they cover:
• How Anthropic's Mythos model can be pointed at a code repository and autonomously surface every vulnerability inside it, and how Anthropic itself estimates Mythos-class capabilities will reach other labs within six to eighteen months, with open-weight versions likely to follow.
• How code-gen models make it easy for attackers by scaling up their capabilities... and by vibe-coders not being aware of vulnerabilities they have!
• How Rubrik's Agent Cloud delivers three pillars of resilience: visibility into every agent in your environment, governance and runtime control through the SAGE small language model, and remediation through Agent Rewind.
• Why the next wave of knowledge work is inherently cross-functional, with A.I. attorneys, security pros, and data scientists all needing shared literacy in A.I. risk.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
In Case You Missed It in April 2026
Whoa, it's May Day... and our podcast-production team was *on the ball* with getting our ICYMI-in-April episode together lickety-split. In case you missed it, these were the best bits of my on-air convos last month:
1. Oracle's Director of A.I. Developer Experience Richmond Alake defines the four types of memory A.I. agents can have... and the biological inspiration for each of them.
2. Matthew J. Glickman, co-founder/CEO of Genesis Computing, describes how A.I. agents allow data engineers to dramatically scale up their impact in an enterprise.
3. The A.I. infrastructure engineer Linda Haviv has amassed a following of over 250,000 folks on social media. In her clip from last month, she combines both worlds — detailing why A.I. infrastructure has now become everyone's problem while also discussing her work in lowering the barrier to access A.I. education.
4. Traci Walker Griffith, principal of The Eliot School in Boston, shares her novel perspective on what critical thinking is... in the context of how fifth-graders are leveraging A.I. to evaluate their work and prepare for tests.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
AI Infrastructure, Ray, and Why Nonlinear Careers Win, with Linda Haviv
For folks in A.I., software, data science, things are moving so fast, it's easy to be overwhelmed. Luckily, A.I. engineer Linda Haviv makes it a joy to stay up to date! Today, we discuss career tips as well as open-source A.I. tech like Ray.
More on Linda:
• Until recently, was Staff Developer Advocate at Anyscale, makers of Ray, an open-source framework for managing, executing and optimizing A.I. compute.
• Previously was A.I. Developer Advocate at Amazon Web Services (AWS).
• Before that, was a software developer at Fox Corporation.
• Was a professional singer in New York up until her second (of three!) children was born.
• Holds a degree in philosophy from Baruch College.
In this episode, Linda ebulliently covers:
• How "A.I. infrastructure" refers to the compute stack, tooling and frameworks purpose-built for A.I. and ML workloads.
• Ray is a Python-native open-source distributed computing framework that lets engineers distribute training, data processing and model serving across GPUs without needing to become distributed systems experts.
• How building in public, creating content and contributing to open source are not just career insurance... they're how you find your community, attract unexpected opportunities and learn faster through teaching.
• And much more!
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
Building Hardware is Hard but AI Agents Help, with Kishore Subramanian
In software, when something goes wrong, you push a patch. In hardware? Oooph. You're dealing with big headaches and huge costs. Thankfully, my guest today — Kishore Subramanian — is using AI to transform the way physical products get built for the better.
Kishore:
• Is CTO of Propel Software, a Bay Area company that combines product data with agentic AI to make the production of physical hardware (including high tech and medtech devices) as seamless as possible.
• Prior to Propel, held senior engineering roles at Google, where he worked on Google Assistant, so he has particularly rich experience with agent development.
• Holds a degree in electronics, computers and process control… as well as a 200-hour yoga-teaching certificate!
In this episode, Kishore covers:
• How product lifecycle management (PLM) is the system that takes a physical product from concept all the way to the customer and beyond.
• How AI agents can review engineering change orders — the hardware equivalent of pull requests — to flag risks, compliance gaps, and downstream impacts before they become expensive problems.
• How Propel built their AI platform, Propel One, on top of Salesforce's Agentforce 360 Platform, which gave them security, governance, data infrastructure, and a reasoning engine out of the box, allowing them to ship in about six months.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
The Four Types of Memory Every AI Agent Needs, with Richmond Alake
To build an effective A.I. agent, getting its memory right is essential. In today's episode, our agent-memory guide is brilliant (and very funny!) machine-learning architect and engineer, Richmond Alake.
More on Richmond:
• Director of A.I. developer experience at Oracle.
• Previously roles include: staff developer advocate for AI/ML at MongoDB, ML architect at Slalom, writer for NVIDIA and computer-vision engineer at Loveshark.
• Holds a master's in ML and robotics from the University of Surrey.
In this episode, Richmond magnificently covers:
• How agent memory is the encapsulation of systems (embedding models, rerankers, databases, and LLMs) that allow AI agents to learn and adapt with new information over time, rather than starting from scratch every session.
• The four types of agent memory (all drawn from human cognition).
• Memory-first agent harnesses.
• Predictions for a flattening of AI engineering roles, where the future developer will need end-to-end understanding of the full agent stack.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
Building AI Agents Where 99.9% Accuracy Isn't Good Enough, with Raju Malhotra
The headlines shout “SaaSpocalypse,” but I don’t buy it. Neither does my guest today, Raju Malhotra, who argues that, thanks to humans collaborating with agents on optimized workflows, the SaaS opportunity is now far bigger than ever before.
More on Raju:
Chief Product & Technology Officer (CPTO) at Certinia, an Austin, Texas-based company whose Professional Services Automation software is used by over 1400 organizations around the world.
Was previously CPTO at PAR Technology and Khoros.
Earlier, spent 12 years at Microsoft working on cornerstone products like Visual Studio .NET.
Holds an MBA from The Wharton School and an undergrad in computer engineering.
In this episode, we cover:
Traditional SaaS isn't dead… instead, it's evolving into a hybrid of SaaS plus agentic capabilities, where humans and agents work together in optimized workflows.
By removing the human-skills constraint from professional services delivery, the agentic revolution could expand the addressable market by 7-8X.
The Agentforce 360 platform (by combining probabilistic AI with deterministic logic and guardrails) empowers innovators to turn their ideas into scalable software businesses, allowing businesses like Certinia to bring AI agents securely and reliably to their customers, even in sensitive industries where 0.1% error rates are unacceptable.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
AI in the Classroom: How a Top Elementary School Is Doing It Right, with Principal Traci Walker Griffith
Long overdue episode today on how A.I. can support children's education. Hard to imagine a better guest than Traci Walker Griffith, principal of a K-8 school that has used innovations like A.I. to become Boston's #1 school.
In this episode, we discuss:
How Traci transformed The Eliot School from an underperforming school on the closure list into the highest-performing school in Boston.
How kids as young as four at the Elliott work with robots and coding tools like Kibo and Scratch Junior, learning that the quality of their input determines the quality of their output ("garbage in, garbage out").
How, for younger students in kindergarten through fourth grade, teachers use A.I. behind the scenes.
How students in grades five through eight interact with A.I. directly, enabling them to build metacognition and critical-thinking skills.
Her concrete guidance for schools (or parents!) considering incorporating A.I. into pedagogy.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
In Case You Missed It in March 2026
It just keeps getting better and better... ICYMI, my on-air conversations with guests in March were extraordinary. Today's episode highlights the best bits from last month, specifically:
Zack Kass (who was head of go-to-market at OpenAI when ChatGPT was launched and who recently wrote bestselling book "The Next RenAIssance") details why classrooms must change in the age of A.I.
Renowned New York University professor KyungHyun Cho explains why A.I. learning to explore the world like humans will unlock major progress in A.I. capability.
Three-time bestselling O'Reilly author Chris Fregly tells us why, if we're still writing code manually in 2026, we're behind the times.
Fireworks AI CEO Lin Qiao explains the difference between artificial general intelligence (AGI) and what she terms "autonomous intelligence".
Acceldata CEO Rohit Choudhary provides a clear vision for how job roles will be transformed by A.I.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
How Data Engineers Are “10x’ing” Themselves With Agents, feat. Matt Glickman
Something big happened in February that changed the world forever. My guest today, Matthew J. Glickman, says code-generating models crossed an event horizon... and there's no turning back. Listen in for the implications.
More on Matt:
Co-founder and CEO of Genesis Computing, a New York-based company building enterprise-ready data agents that automate everything from raw data to production applications, compressing projects that took months into hours while recovering massive hiring costs.
Previously spent over two decades at Goldman Sachs leading analytics and data platform teams, then joined Snowflake as employee 81, where he led Product Management, launched the Snowflake Marketplace, and grew Financial Services into Snowflake’s largest industry vertical.
Holds a degree in Computer Science and Math.
In this episode, which will be fascinating to anyone but especially to hands-on A.I. and data practitioners, we discuss:
How February 2026 marked the moment the latest frontier models crossed a threshold where they could handle complex, multi-step data engineering workflows that previously required human expertise... and there's no going back.
How finance and healthcare were late to adopt the cloud but are among the earliest and most aggressive adopters of A.I.
How Genesis deploys its agentic platform directly inside a client's environment (more like onboarding a new employee than adopting a SaaS product) so that all accumulated knowledge remains the company's asset.
How, rather than acting as a copilot that waits for human instructions step by step, Genesis inverts the model: Agents work autonomously on complex data engineering tasks and only escalate to humans when their confidence is low, memorializing every answer so they never ask the same question twice.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
Attention, World Models and the Future of AI, with Prof. Kyunghyun Cho
What's going to be the next big step function in A.I.? To find out, I saw down with Prof. KyungHyun Cho, who's 200,000 citations put him among the most influential A.I. researchers in the world... and he's a delight to listen to!
In case you aren't already aware of KyungHyun:
Iconic New York University professor of computer science and data science.
Co-directs the Global A.I. Frontier Lab alongside Yann LeCun.
Regularly keynotes at the most prestigious academic A.I. conferences (including being a keynote at NeurIPS 2025).
Was a postdoc under Yoshua Bengio at the Université de Montréal, where they coauthored a paper introducing attention for neural networks, a technique that is ubiquitous within the transformer-based LLMs that enable most A.I. capabilities today.
Lots of other hugely influential papers on deep recurrent neural networks, neural machine translation, visual attention, speech recognition and multivariate time-series modeling.
In today's episode, which will be of particular interest to hands-on A.I. practitioners, KyungHyun eloquently discusses:
The human story behind the invention of attention.
World models.
Why today’s models have already captured most correlations in passive data, making the real challenge about actively choosing which data to collect.
Whether A.I. needs high-fidelity, step-by-step imagination or whether a high-level latent representation that lets it skip ahead is sufficient.
How he's adapting computer-science and A.I. education at the university level now that such capable code-generating agents exist.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
Unmetered Intelligence is Heralding the Next Renaissance, with Zack Kass
Today's episode is one of my fave convos ever. Based on his new bestseller, Zack Kass makes a clear case for why cheap abundant intelligence is heralding the next Renaissance — the greatest period for humans yet.
More on Zack:
Was head of go-to-market at OpenAI from 2021 to 2023, including during the initial public launch of ChatGPT.
Advises Fortune 1000 board rooms, including Coca-Cola, Morgan Stanley and Amgen.
His book, "The Next Renaissance: A.I. and the Expansion of Human Potential", went on sale recently and is already a national bestseller. In it, he argues that A.I. will provide the greatest leap in human history.
Today's episode should be of great interest to any listener. In it, we discuss:
How to actively counter tech pessimism.
Ways AI can transform education for the better.
The "intellectual K-curve" that empowers motivated learners.
His four principles for thriving in the age of A.I.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
AI Systems Performance Engineering, with Chris Fregly
Chris Fregly spent $6000 at Starbucks writing a 1000-page book Nvidia's own docs couldn't provide. In today's episode, the three-time bestselling author reveals all, updating everything you know about engineering A.I. systems.
More on Chris if you don't know him already:
Long-time A.I. systems performance specialist at Amazon Web Services (AWS), where he, for example, pioneered the design and launch of SageMaker and Bedrock.
Was Chief Product Officer at PipelineAI (acquired by AWS).
Previously held Principal Engineer roles at Databricks and Netflix (earning him an Emmy)!
Was an investor/advisor in xAI (acquired by SpaceX) and Groq (acquired by NVIDIA).
Three-time author of O'Reilly books.
His latest book, "A.I. Systems Performance Engineering" is a thousand-page tome that was published in December and has received rave reviews so far.
Today's episode will appeal primarily to hands-on A.I. practitioners like A.I. engineers, data scientists and software developers. You'll learn a ton about GPUs and getting the best performance from them!
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
The Laws of Thought: The Math of Minds and Machines, with Prof. Tom Griffiths
Based on his latest bestseller "The Laws of Thought," today's fascinating episode with Princeton professor Tom Griffiths digs into mathematical models of both biological and artificial intelligence.
More on extraordinary Tom:
• Professor at Princeton University in both the Departments of Computer Science and Psychology.
• Directs Princeton's Computational Cognitive Science Lab (research group focused on understanding the mathematical foundations of human cognition) as well as the Princeton Laboratory for Artificial Intelligence (a new effort that supports innovative research efforts in A.I. and related fields).
• Co-author of the megabestselling book "Algorithms to Live By" (2016) and author of the sensational new book "The Laws of Thought."
• His award-winning research has been published in venues that include the prestigious journals Science and Nature.
In this episode, which will appeal to anyone interested in human intelligence or A.I., Professor Griffiths details how the mathematical principles governing the external world can also be used to explore cognitive science, or “the internal world.”
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
AI for the Physical World, with Samsara's Praveen Murugesan
Samsara processes 20 trillion (!!) data points for physical applications (e.g., construction, transport, manufacturing). Today, their VP Engineering Praveen Murugesan details how they make A.I. impactful on this vast scale.
More on Praveen:
As Vice President of Engineering at Samsara, leads the development and strategy for products that transform IoT data into automations and insights.
Previously worked at Uber, Salesforce, VMware, Cisco and more.
Active angel investor.
Holds an MS from Carnegie Mellon University.
More on Samsara:
Publicly-listed on the New York Stock Exchange.
Recently named one of Fast Company’s Most Innovative Companies.
Ranked #7 on the Fortune "Future 50".
Today's episode will particularly appeal to folks who build A.I. systems hands-on, but will be interesting to anyone keen to understand how A.I. can be developed and deployed to make a huge impact at scale in real-world, physical applications, including on edge devices.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
From PhD Side Project to $500M ARR: Will Falcon’s PyTorch Lightning Story
How did William Falcon grow a PhD side project into an open-source phenomenon with nearly 400 million downloads and a startup with over $500m (🤯) in ARR? Find out in today's episode!
In case you're not already familiar with Will, he's:
Creator of PyTorch Lightning (open-source framework for rapidly training and deploying A.I. models that has been downloaded nearly 400 million times).
Founder and CEO of Lightning AI.
Pursued PhD at New York University under Yann LeCun and KyungHyun Cho focused on biologically inspired deep learning and reinforcement learning techniques, involving pre-training models on 4,000+ GPUs.
Former U.S. Navy officer in SEAL training pipeline.
More on Lightning AI:
The only fullstack A.I. neocloud for enterprises and frontier labs.
$500m in ARR in under two years (hence the head-exploding emoji above!)
3rd largest neocloud by GPUs (35,000+).
Serves 400,000+ developers and companies (e.g., Cursor, Cisco, Reflection AI).
This episode will especially appeal to hands-on practitioners like software engineers and data scientists but Will's fascinating story will be of interest to anyone involved in A.I.
And note that I hold a fellowship at Lightning AI so I am not an unbiased interviewer in this episode :)
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.