
Hosted by Jon Krohn · EN

Chip Huyen joins host Jon Krohn for this milestone episode 999 to talk about her record-breaking book "AI Engineering" the most-read title on the O'Reilly platform last year and how the AI landscape has shifted since her last appearance. Chip breaks down what separates AI engineering from machine learning engineering, makes the case for a "start simple" workflow, gets candid about the real costs of running LLMs in production, and shares why she's now fascinated by physical AI, robotics, and world models and why the durable problems worth solving are increasingly human ones. Jon Krohn guides the conversation from the practical content of the book through to where the field is heading next. Additional materials: https://www.superdatascience.com/999 Interested in sponsoring a SuperDataScience Podcast episode? Email natalie@superdatascience.com for sponsorship information. In this episode you will learn: (06:48) What separates AI engineering from machine learning engineering (14:44) The “start simple” approach: prompting, then RAG, then fine-tuning (18:19) Why web search is so painfully expensive in production (35:11) Is the “ChatGPT moment” for physical AI really here? (52:21) Why the durable problems left to solve are people problems

In this month’s episode of ICYMI, Jon Krohn explores how AI agents are simultaneously creating new risks and unlocking powerful new ways of working with data. Hear from Anneka Gupta, Cal Al-Dhubaib, Trevor Manz, Jazmia Henry, Jeremy Mumford, and Jacob Miller, discussing why the old cybersecurity playbook breaks down in the age of Claude Mythos, how the notebook became an AI agent’s working memory, what it really takes to build a foundation model from scratch, and why failing slowly is the most expensive mistake an AI team can make. Additional materials: www.superdatascience.com/998 Interested in sponsoring a SuperDataScience Podcast episode? Email natalie@superdatascience.com for sponsorship information. In this episode you will learn: (00:40) Why Claude Mythos Changes Everything About Cybersecurity (08:11) Why Your Notebook Should Be Your Agent’s Working Memory (13:19) What It Actually Takes to Build a Foundation Model From Scratch (20:46) Failing Slowly Is the Most Expensive AI Mistake

Dr. Andrey Kurenkov returns to the show to talk about Astrocade's astronomical growth from pre-alpha to over 20 million engaged users, what it actually takes to build a vibe-coding platform that scales, and how the broader AI landscape has shifted since his last appearance. Andrey shares behind-the-scenes lessons from building B2C user-generated content products, why the real moat is community rather than tech, and his current thinking on humanoid robotics, AGI, and the AI risks people actually overlook. Additional materials: https://www.superdatascience.com/997 Interested in sponsoring a SuperDataScience Podcast episode? Email natalie@superdatascience.com for sponsorship information. In this episode you will learn: (02:11) The Astrocade elevator pitch and how it grew to 20M users (16:19) Why there's no secret sauce behind the platform (24:56) UGC as the real moat, not the AI (46:57) Why household humanoid robots are now 2–3 years away (58:33) What AGI actually means, and why Andrey is an ASI skeptic

TrueFoundry co-founder and CEO Nikunj Bajaj speaks to Jon Krohn about how enterprises like Nvidia and Siemens are realizing returns of over $100 million from single agent deployments, the AI gateway architecture that makes it possible to connect, observe, and govern agents at scale, and why the familiar advice to “start small” is the wrong way to roll out AI agents inside a large organization. Additional materials: www.superdatascience.com/996 Interested in sponsoring a SuperDataScience Podcast episode? Email natalie@superdatascience.com for sponsorship information. In this episode you will learn: (01:21) What TrueFoundry does and why agents in production need a control plane (06:32) Breaking down the AI gateway: the model, MCP, and agent gateways (16:47) Taming tool sprawl with scoped, read-only MCP access (19:10) Why the agent gateway is the hard part and the kill switch most teams lack (22:24) The five-workflow framework behind $100M agent deployments

Jazmia Henry joins Jon Krohn to break down what it actually takes to build end-to-end foundation models for the energy industry. From wrangling decades of handwritten oil-and-gas documents into usable training data, to bespoke tokenizers, reinforcement learning, and inference at scale, Jazmia walks through every stage of the stack. Along the way she explains why reinforcement learning models are "bursty," what reward hacking is and how her Grounded Continuous Evaluation framework fixes it, and revisits the 2023 NeurIPS paper that argued, to widespread skepticism at the time, that scaling bad data degrades model performance. Additional materials: https://www.superdatascience.com/995 Interested in sponsoring a SuperDataScience Podcast episode? Email natalie@superdatascience.com for sponsorship information. In this episode you will learn: (10:06) The User Agnosticism Tenet (20:02) The Zillow Offers parable (23:25) Why workflows should come before agents (29:57) Why data engineering is the bedrock of AI (52:41) Why velocity is the only durable moat

Unemployment for recent computer-science graduates now rivals rates for fine-arts and anthropology majors, and undergraduate CS enrollment fell 11% in 2025. In this Five-Minute Friday, Jon Krohn walks through the data on both sides of the debate, from Stanford research showing a 13% employment drop for young workers in AI-exposed jobs, to Federal Reserve studies finding no statistically detectable link between AI adoption and reduced hiring. Jon shares his own view on where the truth lies and offers five concrete pieces of advice for graduates and senior professionals alike on how to get hired in 2026. Additional materials: www.superdatascience.com/993 Interested in sponsoring a SuperDataScience Podcast episode? Email natalie@superdatascience.com for sponsorship information.

For years, AI content has come in the form of “use this library, use this tool” tutorials that age out within months. Jacob Miller and Jeremy Mumford, co-authors of the brand new Wiley book Architected Intelligence, wanted to write something different, a guide to the higher-level principles of building AI products and AI-first organizations that will still be relevant in five or ten years. In this episode, the two Pattern engineers walk Jon Krohn through the core ideas of their book: why you should design products and processes so they can be executed by a human, an AI agent, or any hybrid combination; why most companies are still treating hallucinations as a model problem when they’re actually a data curation problem; why the natural progression of AI development goes skills, workflows, agents, not straight to agents; and why velocity, not models or data, is the only durable competitive advantage left. Additional materials: https://www.superdatascience.com/993 Interested in sponsoring a SuperDataScience Podcast episode? Email natalie@superdatascience.com for sponsorship information. In this episode you will learn: (10:06) The User Agnosticism Tenet (20:02) The Zillow Offers parable (23:25) Why workflows should come before agents (29:57) Why data engineering is the bedrock of AI (52:41) Why velocity is the only durable moat

While “tokenmaxxing”, the social media trend of maximizing AI token consumption as a vanity metric, takes off online, the physical infrastructure behind AI is slamming into serious bottlenecks. In this Five-Minute Friday, Jon Krohn maps out the four overlapping supply-chain constraints choking AI compute: GPUs (with NVIDIA Blackwell sold out through mid-2026), high-bandwidth memory (quintupled demand since 2023, only three manufacturers worldwide), CPUs (agentic AI requires 12x more CPUs per GPU than chatbots), and electricity (Gartner projects power shortages will restrict 40% of AI data centres by 2027). Find out why the five biggest hyperscalers are on track to spend $725 billion on AI infrastructure in 2026, where the reasons for optimism lie, and why Jon says you should definitely not tokenmaxx. Additional materials: www.superdatascience.com/992 Interested in sponsoring a SuperDataScience Podcast episode? Email natalie@superdatascience.com for sponsorship information.

Dr. Trevor Manz of Marimo talks to Jon Krohn about Marimo Pair, an open-source agent skill that teaches coding agents like Claude Code how to drive a reactive Python notebook, reading cell state, running Python in the kernel, taking screenshots of cells, and iterating on data tasks the way agents iterate on traditional software. Trevor also unpacks recursive language models, his AnyWidget project that bridges Python and the web, and his journey from a Wisconsin small town and Harvard bioinformatics research to founding-engineer life at Marimo. Listen to the episode to hear why no matter where AI takes us, curiosity and going deep on a topic will always be valuable. Additional materials: www.superdatascience.com/991 Interested in sponsoring a SuperDataScience Podcast episode? Email natalie@superdatascience.com for sponsorship information. In this episode you will learn: (07:04) What Marimo Pair is and how it teaches agents to use notebooks as a tool (13:03) How agent skills work as folders of markdown files (24:15) Trevor's day-to-day workflow combining Claude Code and Marimo Pair (31:51) Recursive language models and why they could be the future of agentic reasoning (57:33) Career advice on curiosity, going deep, and becoming a domain expert

Anthropic has built a frontier AI model so capable at finding software vulnerabilities that it has decided not to release it to the general public. In this Five-Minute Friday, Jon Krohn breaks down Claude Mythos Preview, a general-purpose model whose hacking abilities emerged as a side effect of broad improvements in code understanding and reasoning. Find out how Mythos achieved a nearly 100x improvement over Opus 4.6 on Firefox exploit generation, why Mozilla patched 271 vulnerabilities in a single release using an early version of the model, and what Project Glasswing Anthropic’s gated industry consortium means for the future of cybersecurity. Jon also shares practical tips for securing the code you’re generating with AI tools. Additional materials: www.superdatascience.com/990 Interested in sponsoring a SuperDataScience Podcast episode? Email natalie@superdatascience.com for sponsorship information.