Super Data Science: ML & AI Podcast with Jon Krohn

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 [email protected] 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 [email protected] 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 [email protected] 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 [email protected] 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 [email protected] 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 [email protected] 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 [email protected] for sponsorship information.

Rubrik’s Anneka Gupta and Cal Al-Dhubaib speak to Jon Krohn about cybersecurity measures, the risks AI in business might pose for malicious attacks, and why AI should be kept “boring.” Find out how Rubrik safeguards client data, what zero trust is in the context of cybersecurity, and why cyber-resilience needs to be a top priority for companies looking to adopt AI. Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/989⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information. In this episode you will learn: (02:25) All about Rubrik                                   (08:51) The announcement of Claude Mythos              (26:26) Utilizing zero trust                   (40:36) About the Rubrik agent cloud

In this month’s episode of In Case You Missed It, Jon Krohn talks to guests about memory and education, and how artificial intelligence is continuing to help lower the barriers to access. Hear from Matt Glickman, Traci Walker-Griffith, Richmond Alake, and Linda Haviv, discussing the foundations of AI agent memory, how engineers can develop at scale, and why they believe AI could be your child’s perfect tutor in the classroom. Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/988⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information.

Linda Haviv talks to Jon Krohn about staying current on AI matters, why open-source technology is narrowing the gap in its race with proprietary models, and how being a content creator in tech is key to career growth and longevity. She emphasizes that non-linear pathways to a career in tech can give applicants an edge, and stresses the importance of continuous upskilling to “stay relevant.” In her view, systems thinking is becoming more important than coding skills. Hear why in this episode. Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/987⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information. In this episode you will learn: (03:43) Linda Haviv on AI education   (13:16) The future of coding (27:00) Having a side hustle in today’s economy         (31:01) On becoming a content creator for tech (1:00:14) How open source could disrupt the AI landscape

CTO of Propel Software Kishore Subramanian talks to Jon Krohn about how product lifecycle management (PLM) software and quality management systems (QMS) help ensure compliance, record management, and quality assurance. Listen to the episode to hear Kishore Subramanian talk about best practices for getting started with Agentforce 360, his top tips for deploying AI projects, and why yoga and meditation could make you better at building AI products! Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/984⁠⁠⁠⁠⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information.⁠⁠⁠ In this episode you will learn: (05:21) How Propel Software meets its customers’ demands (07:57) About Propel One AI  (13:31) A case study for Salesforce’s Agentforce 360 Platform (17:08) How to build an enterprise-ready agent with Agentforce 360 (19:21) How to get your AI tool into production

Oracle’s Director of AI Developer Experience Richmond Alake returns to the show to talk to Jon Krohn about agent memory; the network of systems, models, databases and LLMs that enable AI agents to learn and adapt over time. Listen to the episode to hear about Richmond’s “100 Days of Agent Memory” initiative, retrieval-augmented generation’s (RAG) limitations with AI agents, the layers of the AI agent stack, and what makes the Oracle AI database so useful to developers. Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/985⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information. In this episode you will learn: (03:15) What agent memory is and why it’s important (28:28) RAG’s limitations for AI agents  (35:19) What matters in the AI agent stack beyond memory (41:34) Why memory was undervalued in the AI agent stack

Raju Malhotra, Chief Product and Technology Officer at Certinia, talks to Jon Krohn about the so-called SaaSpocalypse and how agentic AI is proving the doomsayers wrong. Listen to the episode to hear more about Certinia’s work with Salesforce and building with Agentforce 360, the three elements required for enterprise-grade agents, how AI agents have benefitted Certinia’s customers, and how to keep your work portfolio fresh and interesting to recruiters. Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/984⁠⁠⁠⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information.⁠⁠⁠ In this episode you will learn: (01:24) What Certinia does for professional services companies (08:45) Why the "SaaSpocalypse" is wrong (13:19) Agentforce 360 and how Certinia builds on it (15:06) The three elements required for enterprise-grade agents (18:02) How AI agents have impacted Certinia's customers

My guest today took a public school that was about to be shut down and turned it into the number one school in Boston, and AI is her latest secret weapon. In a long-overdue episode on AI for supporting children’s education, hear directly from Principal Traci Walker Griffith how her teachers have been experimenting with AI in classrooms, what works, what doesn’t work, and what’s next for kids as LLMs continue to improve. Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/983⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information. In this episode you will learn: (03:38) The Eliot School’s transformation from closure list to number one in Boston (08:54) How the school began using Claude for AI-assisted writing feedback (18:14) How younger students benefit from AI behind the scenes (23:46) How older students interact with AI directly (41:11) Three prompt engineering failure modes and how to fix them (55:29) Responding to the Brookings report on AI risks in education

Jon Krohn rounds up March’s interviews in this ICYMI episode. Hear from AI and data science experts across the fields of education and business in this wide-ranging series of clips that take listeners from the Renaissance to the near future. Guests include Lin Quiao (Episode 971), Chris Fregly (Episode 973), Zack Kass (Episode 975), Kyunghyun Cho (Episode 977), and Rohit Choudhary (Episode 979).   Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/982⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information.

Matt Glickman talks to Jon Krohn about co-founding the agentic-platform startup, Genesis Computing, how his experience at Goldman Sachs paved the way for developing AI agents, and where he thinks agentic AI has just as much value as a company’s human employees. This February, Genesis Computing revealed how its platform can offer the guardrails so crucial to businesses, alongside increased capabilities that help execute entire workflows from research to deployment. Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/981⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information. In this episode you will learn: (12:56) Cloud adoption in finance and healthcare (18:28) How Genesis Computing uses AI agents  (31:05) AI agents replacing humans in the workplace  (56:25) An argument for encouraging enterprises to use AI

A team of theoretical physicists from Harvard, Cambridge, the Institute for Advanced Study, and Vanderbilt used OpenAI’s models not just as a tool, but as a collaborator, cracking a problem in particle physics that had stymied them for months. In this Five-Minute Friday, Jon Krohn walks through how GPT-5.2 Pro simplified a 32-variable mathematical expression into a single line, proposed what it called the “obvious generalization” for any number of gluons, and how a more powerful internal model then produced a formal proof after 12 hours of autonomous reasoning. Find out why this may be a template for AI-assisted scientific discovery and what it means for the future of research. Additional materials:⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/980⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information.

For years, Jon has been quoting the stat that the world's data is roughly doubling every year. His guest today says that’s way too conservative, he’s seeing enterprise data soon growing at close to 10x per year. And most organizations are nowhere near ready for what that means. In this episode, Rohit Choudhary, founder and CEO of Acceldata, explains how the agentic data management platform his team has built helps enterprises make their increasingly vast amounts of data self-aware, self-optimizing, and AI-ready. He breaks down why governance needs to be operational and real-time rather than a one-time compliance exercise, and shares his view on why the most valuable professionals in the age of AI won’t be the best programmers, they’ll be the ones with the clearest thinking and the deepest domain expertise. Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/979⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information. In this episode you will learn: (03:26) How Rohit coined the term “data observability” (06:04) Agentic data management use cases (12:46) Why fixing data at the point of consumption is 1000x more expensive (30:49) Career paths and skills for the age of AI (42:38) Why enterprise data will soon grow at nearly 10x per year

A game millions of people solve over morning coffee is exposing a fundamental weakness in today’s most powerful AI models. In this Five-Minute Friday, Jon Krohn breaks down Pathway’s new Sudoku Extreme benchmark, roughly 250,000 of the hardest Sudoku puzzles available and why leading LLMs like o3-mini, DeepSeek-R1, and Claude 3.7 Sonnet scored effectively zero percent, while Pathway’s post-transformer BDH architecture achieved 97.4% accuracy at a fraction of the cost. Listen to the episode to find out what BDH is doing differently, why Sudoku performance matters far beyond puzzles, and what this means for the future of AI reasoning. Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/978⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information.

What’s going to be the next big step function that blasts us forward in AI capabilities? To find out, Jon Krohn sits down with Professor Kyunghyun Cho, whose 200,000 citations and co-authorship of the first paper on attention place him among the most influential AI researchers in the world. In this episode, Kyunghyun explains why today’s models have already captured most correlations in passive data, making the real challenge about actively choosing which data to collect. He also weighs in on the open debate around world models, whether AI needs high-fidelity, step-by-step imagination or whether a high-level latent representation that lets it skip ahead is sufficient and shares the surprising discovery that 80% of his 200 computer science students had never installed a coding agent. Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/977⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information. In this episode you will learn: (06:43) The story behind the attention mechanism (28:43) Sample efficiency and active data collection (39:04) World models and latent planning (49:52) Teaching undergrads with coding agents (58:21) Reranking, multi-stage ranking, and the foundations of RAG

NVIDIA just dropped Nemotron 3 Super, a 120-billion-parameter open-weight model that only activates 12 billion parameters at a time and it’s built for the agentic AI era. In this Five-Minute Friday, Jon Krohn breaks down the model’s hybrid Mamba-Transformer architecture, its million-token context window, and why its combination of frontier-class reasoning with blazing-fast throughput matters for anyone building multi-agent systems. Find out how Nemotron 3 Super claimed the #1 spot on the DeepResearch Bench leaderboards, which companies are already adopting it, and where you can start using it today. Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/976⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information.

Zack Kass speaks to Jon Krohn about his bestselling, tech-positive book, The Next Renaissance, that charts the rapid progress of humanity and the benefits that artificial intelligence will bring to us, as well as why a future where intelligence is a cheap and abundant resource will give humanity an edge. Elsewhere in the show, Zack discusses why it’s important to hold parents, teachers and students accountable for their education, why it is incumbent on us to build a healthier relationship with technology, and his 4 principles for thriving in the age of AI. This episode is brought to you by the⁠ ⁠⁠Cisco⁠, by ⁠Acceldata⁠ and by ⁠⁠ODSC, the Open Data Science Conference⁠⁠. Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/975⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information. In this episode you will learn: (03:14) About Zack Kass’ book, The Next Renaissance (20:18) The importance of literacy skills in the age of AI (28:01) AI in education (41:01) Principles for living in the era of AI

In this week’s Five-Minute Friday, Jon Krohn holds the AI bubble up to the light. He points to the deep greyzone found in AI startups like Cluely that are established on dubious ideas (Cluely’s tagline was “cheat on everything”) and funding bluster, as well as the staggering spending by companies on infrastructure and researcher salaries. Listen to the episode to hear about the historical precedents to the AI bubble that go all the way back to the invention of the railway, what to make of current investments in AI, and what you can do about these changes as an AI practitioner. Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/974⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information.

No one should be manually writing code in 2026, thinks Chris Fregly, Jon Krohn’s guest on this week’s episode. In this interview about Chris’ latest book, AI Systems Performance Engineering, he explains why it’s so important to consider memory bandwidth when evaluating GPU performance, that understanding the full hardware software stack is the most valuable skill for anyone working in AI development, and which shortcuts we still shouldn’t ever take when writing code, even though we might be outsourcing a great deal to generative AI. This episode is brought to you by the ⁠⁠Cisco, by Acceldata and by ⁠ODSC, the Open Data Science Conference⁠. Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/973⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information. In this episode you will learn: (03:39) Why Chris wrote AI Systems Performance Engineering  (21:39) Essential coding metrics  (37:24) The importance of inference when coding (42:11) How to manage workflows while using AI agents (51:37) Where and how to invest in the AI market

Jon Krohn recaps the month of February in this episode of In Case You Missed It. Across four interviews with Will Falcon (Episode 965), Tom Griffiths (Episode 969), Antje Barth (Episode 963), and Praveen Murugesan (Episode 967), Jon questions the brains behind some of the AI industry’s most innovative companies about launching a startup, developing a popular product, what artificial intelligence can still learn from human intelligence, and how AI might finally start to think on its own. Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/972⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information.

Lin Qiao, CEO of Fireworks AI, talks to Jon Krohn about how she builds effective models quickly, why coding agents can perform at the level of a junior engineer, and what she attributes to the success of Fireworks AI: True to its name, the company exploded into the AI industry with over $300 million secured in venture capital, as well as netting a further $250 million Series C funding. For Lin, many enterprises miss out by not being familiar with open models. Open models give a lot of control to the user, offering customizability and at a much lower price point. Listen to hear how Fireworks AI helps companies continue to save money through AI. This episode is brought to you by the⁠ ⁠⁠⁠⁠Dell⁠⁠⁠, by⁠ ⁠⁠Intel⁠⁠⁠, by ⁠Cisco⁠ and by ⁠Acceldata⁠. Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/971⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information. In this episode you will learn: (01:19) All about Fireworks AI (24:16) Why companies need to take notice of open models (33:05) The commercial viability of slow-reasoning models (38:51) Fireworks AI’s approach to model performance evaluations

Working with code-gen models and Claude Code: In this Five-Minute Friday, Jon Krohn addresses how AI superstars like Andrej Karpathy are using AI agents in their coding work, the outlook for code-gen in 2026, and how you can get started. Hear about Karpathy’s work as well as the soaring success of Peter Steinberger and how he managed to surpass the GitHub commit rate of teams as an individual working with AI agents. Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/970⁠⁠⁠⁠⁠⁠⁠⁠⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information.

Princeton Professor Tom Griffiths talks to Jon Krohn about his new book, The Laws of Thought, which grapples with the mathematical models behind biological and artificial intelligence, and what makes the human brain so fascinating for psychologists and computer scientists to study. In this episode, he details how the mathematical principles governing the external world can also be used to explore cognitive science, or “the internal world.”  This episode is brought to you by the ⁠⁠Dell⁠⁠, by ⁠⁠Intel⁠⁠, by Cisco and by Acceldata. Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/969⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information. In this episode you will learn: (01:18) Tom Griffiths’ current research (21:23) On mathematical inference in LLMs (35:19) How to engineer inductive bias (52:00) How to model curiosity into AI systems

Now that AI agents can develop new apps from product development to delivery, do AI developers have reason to worry about their careers? Podcast host Jon Krohn addresses the stark predictions that AI could “eliminate half of all entry-level white-collar jobs” by going back to the data. Find out why the numbers show a very different picture, which in-demand occupations have increased by 40% since late 2022, and Jon’s advice on why technical professionals shouldn’t panic in this latest Five-Minute Friday.  Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/968⁠⁠⁠⁠⁠⁠⁠⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information.

VP of Engineering at Samsara Praveen Murugesan talks to Jon Krohn about processing 20 trillion data points covering 90 billion miles across private and public sectors, how the company helps truckers who operate long hours and travel for long stretches without cellphone signal, and who they’re looking to hire to help this physical AI pioneer keep on developing high-impact solutions for real-world problems. And, if you’re looking to work for the company, there’s no better time to apply, and you’ll want to listen to the end of the show to hear exactly what Praveen looks for in new hires. This episode is brought to you by the ⁠⁠Dell⁠⁠, by ⁠⁠Intel⁠⁠, by Acceldata and by the ODSC, the Open Data Science Conference⁠. Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/967⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information. In this episode you will learn: (01:01) The challenges of working with logistics data  (16:43) Operating Edge AI in logistics and construction sectors (28:43) How quantum computing might redefine logistics (40:09) The real cost of swapping human heuristics for algorithmic planning (44:45) How to get a job at Samsara

Jon Krohn gives Five-Minute Friday listeners all the details about the new social network causing a stir, Moltbook. What makes Moltbook so unique is that this is the first network designed just for AI agents. It’s an exclusive club, only its alleged 1.5 million registered agents can post, comment, and upvote, but we can watch this real-world experiment in agent ecology from the sidelines. Listen to the episode to hear the fascinating, if disturbing, story of Moltbook’s swift turn into facilitating a digital theocracy and forms of government, and whether this development is a sign of an approaching singularity or rather AI continuing to ape human thought and turn it into slop.  Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/966⁠⁠⁠⁠⁠⁠⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information.

CEO of Lightning AI Will Falcon speaks to podcast host and Lightning AI fellow Jon Krohn about the company’s merger with Voltage Park, and why Will has named it the “full-stack AI neo-cloud for enterprises and frontier labs”. Lightning AI’s offer is a secure, flexible, and collaborative environment that can run on the cloud, all essentials for early-stage startups. Listen to the episode to hear Will Falcon discuss Lightning AI Studio, founding PyTorch Lightning, and how he came to found his AI company. This episode is brought to you by the Dell⁠⁠, by ⁠⁠Intel⁠⁠, by Fabi and by Cisco. Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/965⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information. In this episode you will learn: (02:20) Lightning AI’s merger with Voltage Park (20:54) About neo-clouds (43:51) How Will founded Lightning AI (54:48) Current gaps in the AI in workplace

In this first of the year ICYMI episode, Jon Krohn selects his favorite moments from January’s SuperDataScience interviews. Listen to why incentivizing workers is the best way to get them to disclose their use of AI tools and pave the way for an AI-forward future, how AI continues to mimic human development in its own evolution, the importance of evaluation in building AI systems, and how to keep your best employees (and also: how to know your value) with guests Sadie St. Lawrence, Ashwin Rajeeva, Sinan Ozdemir, Vijoy Pandey, and Ethan Mollick. Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/964⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information.

Bestselling author and Gen AI instructor Antje Barth talks to Jon Krohn about her work at Amazon’s AGI Labs and their newest product Nova Act, as well as where we will see the most success with AI agents and how AI developers can reap those rewards.  This episode is brought to you by the ⁠⁠Dell⁠⁠, by ⁠⁠Intel⁠⁠, by Fabi and by Cisco. Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/963⁠⁠⁠⁠⁠⁠⁠⁠⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information. In this episode you will learn: (01:23) Amazon’s latest product, Nova Act (11:05) How Nova Act tests reliability (24:01) Where Amazon’s 1000s of gen AI deployments succeed (31:32) How Nova Act maintains its security (36:32) The increasing value of agentic AI developers

Bestselling author of Co-Intelligence: Living and Working with AI Ethan Mollick speaks to Jon Krohn about just how much US firms have to gain from a willingness to adopt and experiment with AI, as well as the reality behind AI use among employees and the frontier models set to support them even further.  Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/962⁠⁠⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information.⁠⁠⁠

Dr. Vijoy Pandey returns to the show to talk to Jon Krohn about Cisco’s work to advance medicine and mitigate the impact of climate change with distributed artificial super-intelligence. Dr. Vijoy Pandey believes in a future where humans and AI agents work together to tackle our biggest challenges. For this to happen, we will need to have multi-agent systems and open-source platforms that let agents work together, avoiding the phenomenon of AI agents being “isolated geniuses” unable to collaborate. He elaborates on what Cisco is doing to close this gap. This episode is brought to you by the⁠ ⁠⁠Dell⁠⁠⁠, by⁠ ⁠⁠Intel⁠⁠⁠, by ⁠Fabi⁠ and by ⁠⁠⁠⁠Scaylor⁠. Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/961⁠⁠⁠⁠⁠⁠⁠⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information. In this episode you will learn: (03:55) A definition of artificial super-intelligence (14:03) Distributed learning through Cisco’s Outshift (21:29) The semantic protocols for sharing intent in a distributed artificial super-intelligence framework (37:44) The cognitive memory fabric of the distributed artificial super-intelligence framework (46:24) Using cognitive engines as part of the distributed artificial super-intelligence framework

For 2026’s first episode of In Case You Missed It (ICYMI), Jon Krohn selects 6 clips from December for a wide-ranging look at the current state of AI in business and beyond. Hear from Joel Beasley (Episode 945), Jeff Li (Episode 947), Sandy Pentland (Episode 949), Josh Clemm (Episode 951), Penelope LaFeuille (Episode 952), and John Roese (Episode 953) on ensuring your AI systems get adopted and succeed in their goals, interesting ways to use AI for standup comedy routines, and more. Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/960⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information.

AI entrepreneur and bestselling author Sinan Ozdemir speaks to Jon Krohn about the practical differences between agentic AI and AI workflows, why evaluating accuracy on its own won’t tell you enough about AI models, and more about his latest book Building Agentic AI.  This episode is brought to you by the ⁠⁠Dell⁠⁠, by ⁠⁠Intel⁠⁠, by Fabi and by Cisco. Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/959⁠⁠⁠⁠⁠⁠⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information. In this episode you will learn: (04:57) Exploring the differences between workflows and agents (17:03) How to work out parameter count for a given task (25:26) The best way to evaluate LLMs (33:12) How to run hybrid workflow + agentic projects effectively

In this #sponsored Feature Friday episode, Salesforce’s Rahul Auradkar speaks to Jon Krohn about the company’s unified data engine and how its acquisition of Informatica provides the missing context layer for AI models and agents. Hear how Salesforce’s Data 360 helps customers to get accurate and insightful information about their business, and what AI models need to benefit a company’s bottom line (hint: it’s not only large amounts of data!) Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/958⁠⁠⁠⁠⁠⁠⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information.

AI agents, data lakes, and managing data sprawl: Ashwin Rajeeva, cofounder and CTO of Acceldata, speaks to Jon Krohn about how the agentic data management startup raised over $100 million in venture capital to expand its business in automating data quality assurance as well as cataloguing and pipeline maintenance across enterprise environments. Acceldata utilizes multiple agents to solve enterprise-grade questions with company data. It also uses autonomous data pipelines that can detect and fix issues without human intervention, and the platform’s agentic data management system ADM also lets humans stay in the loop wherever needed.  This episode is brought to you by the ⁠⁠Dell⁠⁠, by ⁠⁠Intel⁠⁠, by Fabi and by Cisco. Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/957⁠⁠⁠⁠⁠⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information. In this episode you will learn: (03:25) About Acceldata and xLake (15:03) Autonomous data pipelines (21:02) How and when to keep humans in the AI loop (27:43) How Acceldata solves ‘data sprawl’ (31:53) Habits of successful tech leaders

#Sponsored SVP, Head of Product for Salesforce’s AppExchange & Ecosystem, Tyler Carlson, talks to Jon Krohn about taking AI agents from prototype to enterprise-grade production with the Agentforce 360 Platform. Though we may now have plenty of tools to build demos for AI agents, most teams still struggle to turn early prototypes into secure and scalable products. With Salesforce’s Agentforce 360 Platform, users can build customer-focused agentic applications with a multi-LLM planner service for reasoning and logic, as well as a new scripting language for deterministic control over how agents interact with the contextual layer of the Salesforce data model. Learn how Salesforce’s WYSIWYG schema builder helps customers build fully functional applications with low- and pro-code capabilities, the common mistakes that innovators make when moving from prototype to production, and whether AI agents might replace non-agentic AI applications in this Feature Friday. Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/956⁠⁠⁠⁠⁠⁠⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information.

Sadie St Lawrence joins Jon Krohn to discuss what to expect from the AI industry in 2026. Sadie and Jon talk through what they think will be the five biggest trends in AI, hand out awards for the best moments, comebacks, and disappointments in AI in 2025, and review how their predictions for 2025 played out. Hear Sadie’s five exciting predictions for 2026, from emerging jobs in AI to an important return to the drawing board! This episode is brought to you by the ⁠⁠Dell⁠⁠, by ⁠⁠Intel⁠⁠, by Fabi and by MongoDB. Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/955⁠⁠⁠⁠⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information. In this episode you will learn: (11:36) Recapping Sadie and Jon’s predictions for 2025                      (26:54) The SuperDataScience Awards in AI                                        (49:05) Prediction #1 for AI in 2026                                             (52:13) Prediction #2 for AI in 2026                         (53:33) Prediction #3 for AI in 2026 (57:54) Prediction #4 for AI in 2026                         (1:01:01) Prediction #5 for AI in 2026

Jon Krohn wraps up 2025 with his thoughts on how agentic AI has become as much a resounding success as an annoying buzzword for many in the tech industry, why such promising developments in generative AI mean that well-prepared, secured data will be ever more crucial, and Jon’s hopes for a better year for everyone across the world in 2026.  Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/954⁠⁠⁠⁠⁠⁠⁠⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information.

Dell Technologies’ John Roese talks to Jon Krohn about the phenomenon of “agent-washing”, his contribution to Dell’s incredible revenue boost in 2025, and why “knowledge layers” will be crucial to future tech. Hear also John’s predictions for where AI is going to lead us in 2026, from better, clearer governance, data management methods and definitions for agentic AI, to systems that keep AI tools and our data running and secure with the help of “AI factories” and “sovereign AI”.  This episode is brought to you by MongoDB and by Y Carrot. Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/953⁠⁠⁠⁠⁠⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information. In this episode you will learn: (01:31) John Roese’s predictions for AI in 2026 (11:28) How John increased ROI at Dell Technologies (18:59) John’s predictions for AI in 2026 (40:13) How Dell’s clients are using AI factories

“The Fit Data Scientist” newsletter author Pénélope Lafeuille talks to Jon Krohn about how to give your all at work, offering her top tips for a healthy body and a healthy mind. Learn why “The SuperDataScience Podcast” made it onto her top 3 data science podcasts, and why following your passion can pay off in dividends for your career. Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/952⁠⁠⁠⁠⁠⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information.

VP of Engineering at Dropbox Josh Clemm speaks to Jon Krohn about consolidating search tools across apps with the AI-powered workspace, Dropbox Dash, the new collaborative AI systems that enhance interoperability between team members and their projects, and how to avoid “context rot”. Dropbox Dash gives users the best of Dropbox’s cloud storage and search functions, plus a “universal search” ability to locate information across multimedia and apps. “AI really needs to understand you and your team, first and foremost, and all that connected data,” says Josh. This episode is brought to you by the ⁠⁠Dell⁠⁠, by ⁠⁠Intel⁠⁠, by Airia and by MongoDB. Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/951⁠⁠⁠⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information. In this episode you will learn: (01:07) All about Dropbox Dash (10:00) The benefits of browser-embedded AI (22:17) Why context engineering is so critical to agentic systems  (37:51) How creating apps helps tech leadership  (48:39) When to decide to use data versus intuition

In this special holiday episode, the SuperDataScience Podcast team comes together to wish you happy holidays and thank you for listening throughout the year. Team members from around the world share warm greetings in their own voices and languages as we reflect on another year of learning, curiosity, and community. From all of us at SDS, we wish you a joyful holiday season and look forward to bringing you more data science, machine learning, and AI content in the year ahead. Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/950⁠⁠⁠⁠⁠⁠⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information.

Alex “Sandy” Pentland, Toshiba Professor of Media Arts & Science at MIT and Fellow at Stanford, speaks to Jon Krohn about his new book, Shared Wisdom, why he attributes AI to the collapse of the Soviet Union, and why those risks to society could still be relevant today. We can only achieve better system performance, Alex says, when we build tools that keep step with the way that people make decisions. Listen to the episode to hear Alex talk about how he is helping make AI agents work for individuals rather than the companies that develop them, and his work in making sure that systems operate consistently and fairly across the world. This episode is brought to you by the⁠ ⁠⁠⁠⁠Dell⁠⁠⁠, by⁠ ⁠⁠Intel⁠⁠⁠, by ⁠Fabi⁠, and by ⁠Airia⁠. Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/949⁠⁠⁠⁠⁠⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information. In this episode you will learn: (02:19) About Alex Pentland’s new book, Shared Wisdom                     (16:00) About loyalagents.org (28:36) Why we need data unions (34:02) The governance of AI (41:24) How to measure the social impact of AI projects

In this November episode of “In Case You Missed It” series, Jon Krohn selects his favorite clips from the month. Hear from Shirish Gupta and Tyler Cox (Episode 939), Vikoy Pandey (Episode 941), Marc Dupuis (Episode 937), and Maya Ackerman (Episode 943) on getting back to human motivation and the importance of evaluating the tools and data we use.  Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/948⁠⁠⁠⁠⁠⁠⁠⁠⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information.

Jeff Li tells Jon Krohn what it's like to work at scale as a data scientist and a machine learning engineer at Netflix, Spotify and DoorDash, as well as how to get a foot in the door at these companies. Jeff also discusses how to run forecasts and trends, and how to read their results. Listen to hear Jeff Li discuss how Spotify became a podcast powerhouse, his startup move.ai, and the tools he uses every day. This episode is brought to you by the ⁠⁠Dell⁠⁠, by ⁠⁠Intel⁠⁠, by Fabi, and by Airia. Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/947⁠⁠⁠⁠⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information. In this episode you will learn: (09:05) Forecasting in data science                (23:33) How to get a data science job at Netflix     (30:06) Jeff’s experience on launching an AI startup      (51:57) Jeff’s AI toolkit