1006: In Case You Missed It in June 2026
In this month's episode of ICYMI, hear from Chip Huyen, Andrey Kurenkov, Frank Basso and Gilbert Eijkelenboom, discussing why moats are shifting toward physical systems and accumulated product intuition, how Astrocade built vibe coding before the term existed, what it's really like inside a deafeningly loud AI data center, why only 15% of people are technically self-aware and whether AGI requires anything like consciousness.
Additional materials: www.superdatascience.com/1006
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In this episode you will learn:
(00:00) The Cost of Building Software Is Going to Zero — Now What?
(10:18) We Built Vibe Coding Before Anyone Called It That
(21:08) AI Data Centers Are Louder Than a Rock Concert
(28:39) Why 85% of Data Scientists Can't Communicate Their Work
(33:46) Are Humans Also Just Predicting the Next Token?
1003: Building an AI Data Center End to End, with Lightning AI’s Frank Basso
Frank Basso, VP of Infrastructure at Lightning AI, joins Jon Krohn for a rare ground-level tour of the one layer of the AI stack the show had never covered in over a thousand episodes: the physical data center. Frank explains how Lightning AI provisions its 35,000-plus GPUs through hyperscale co-location, why everything new is liquid-to-chip cooled, how GPUs talk to each other over ultra-fast east-west networks, and what it’s actually like to stand inside a 110-decibel AI data hall. He also debunks the most persistent myths about data-center water and electricity use, and makes the case for fuel cells, nuclear power, and 800-volt DC distribution as the path forward.
Additional materials: https://www.superdatascience.com/1003
Interested in sponsoring a SuperDataScience Podcast episode? Email natalie@superdatascience.com for sponsorship information.
In this episode you will learn:
(02:47) What actually makes an AI data center different from a traditional one
(06:04) How Lightning AI provisions its 35,000+ GPUs through hyperscale co-location
(24:01) Why liquid cooling doesn’t waste water, debunking the biggest data-center myth
(29:46) East-west vs. north-south networks, explained
(43:47) “Screaming banshees”: why AI data halls run at 105–110 decibels
(51:52) Why data centers don’t actually drive up your power bill
972: In Case You Missed It in February 2026
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
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AI Trends 2026: OpenClaw Agents, Reasoning LLMs, and More with Sebastian Raschka - #762
In this episode, Sebastian Raschka, independent LLM researcher and author, joins us to break down how the LLM landscape has changed over the past year and what is likely to matter most in 2026. We discuss the shift from raw model scaling to reasoning-focused post-training, inference-time techniques, and better tool integration. Sebastian explains why methods like self-consistency, self-refinement, and verifiable-reward reinforcement learning have become central to progress in domains like math and coding, and where those approaches still fall short. We also explore agentic workflows in practice, including where multi-agent systems add real value and where reliability constraints still dominate system design. The conversation covers architecture trends such as mixture-of-experts, attention efficiency strategies, and the practical impact of long-context models, alongside persistent challenges like continual learning. We close with Sebastian’s perspective on maintaining strong coding fundamentals in the age of AI assistants and a preview of his new book, Build A Reasoning Model (From Scratch).
The complete show notes for this episode can be found at https://twimlai.com/go/762.
[LIVE] Anthropic Distillation & How Models Cheat (SWE-Bench Dead) | Nathan Lambert & Sebastian Raschka
Swyx joined SAIL! Thank you SAIL Media, Prof. Tom Yeh, 8Lee, Hamid Bagheri, c9n, and many others for tuning into SAIL Live #6 with Nathan Lambert and Sebastian Raschka, PhD. Sharing here for the LS paid subscribers.
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965: From PhD Side Project to $500M ARR: Will Falcon’s PyTorch Lightning Story
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 natalie@superdatascience.com 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
#490 – State of AI in 2026: LLMs, Coding, Scaling Laws, China, Agents, GPUs, AGI
Nathan Lambert and Sebastian Raschka are machine learning researchers, engineers, and educators. Nathan is the post-training lead at the Allen Institute for AI (Ai2) and the author of The RLHF Book. Sebastian Raschka is the author of Build a Large Language Model (From Scratch) and Build a Reasoning Model (From Scratch).
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Transcript:
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OUTLINE:
(00:00) – Introduction
(01:39) – Sponsors, Comments, and Reflections
(16:29) – China vs US: Who wins the AI race?
(25:11) – ChatGPT vs Claude vs Gemini vs Grok: Who is winning?
(36:11) – Best AI for coding
(43:02) – Open Source vs Closed Source LLMs
(54:41) – Transformers: Evolution of LLMs since 2019
(1:02:38) – AI Scaling Laws: Are they dead or still holding?
(1:18:45) – How AI is trained: Pre-training, Mid-training, and Post-training
(1:51:51) – Post-training explained: Exciting new research directions in LLMs
(2:12:43) – Advice for beginners on how to get into AI development & research
(2:35:36) – Work culture in AI (72+ hour weeks)
(2:39:22) – Silicon Valley bubble
(2:43:19) – Text diffusion models and other new research directions
(2:49:01) – Tool use
(2:53:17) – Continual learning
(2:58:39) – Long context
(3:04:54) – Robotics
(3:14:04) – Timeline to AGI
(3:21:20) – Will AI replace programmers?
(3:39:51) – Is the dream of AGI dying?
(3:46:40) – How AI will make money?
(3:51:02) – Big acquisitions in 2026
(3:55:34) – Future of OpenAI, Anthropic, Google DeepMind, xAI, Meta
(4:08:08) – Manhattan Project for AI
(4:14:42) – Future of NVIDIA, GPUs, and AI compute clusters
(4:22:48) – Future of human civilization
Why PyTorch Won
At the PyTorch Conference 2025 in San Francisco, Luca Antiga — CTO of Lightning AI and head of the PyTorch Foundation’s Technical Advisory Council — discussed the evolution and influence of PyTorch. Originally designed to be “Pythonic” and researcher-friendly
Antiga emphasized that PyTorch has remained central across major AI shifts — from early neural networks to today’s generative AI boom — powering not just model training but also inference systems such as vLLM and SGLang used in production chatbots. Its flexibility also makes it ideal for reinforcement learning, now commonly used to fine-tune large language models (LLMs).
On the PyTorch Foundation, Antiga noted that while it recently expanded to include projects likev LLM ,DeepSpeed, and Ray, the goal isn’t to become a vast umbrella organization. Instead, the focus is on user experience and success within the PyTorch ecosystem.
Learn more from The New Stack about the latest in PyTorch:
Why PyTorch Gets All the Love
Lightning AI Brings a PyTorch Copilot to Its Development Environment
Ray Comes to the PyTorch Foundation
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831: PyTorch Lightning, Lit-Serve and Lightning Studios, with Dr. Luca Antiga
PyTorch Lightning is revolutionizing the AI landscape, and Dr. Luca Antiga, CTO of Lightning AI, joins host Jon Krohn to explain how. In this episode, they explore the tools pushing AI development forward, from Lightning Studios to Lit-Serve, and discuss the game-changing rise of small language models that challenge industry giants with precision and speed. Luca also shares his vision for developers in an AI-enhanced world, where coding meets creativity and collaboration with intelligent tools.
This episode is brought to you by epic LinkedIn Learning instructor Keith McCormick, and by ODSC, the Open Data Science Conference. Interested in sponsoring a SuperDataScience Podcast episode? Email natalie@superdatascience.com for sponsorship information.
In this episode you will learn:
How Lightning AI's open-source tools make AI development faster [11:30]
The rise of small language models and how they'll rival LLMs [37:47]
Luca's journey from biomedical imaging to deep learning pioneer [52:03]
How AI will transform software developer tasks [1:03:05]
Additional materials: www.superdatascience.com/831
#183 Will Falcon: Lightning Studio, an iOS for AI Developers?
This episode is sponsored by 1Password. 1Password combines industry-leading security with award-winning design to bring private, secure, and user-friendly password management to everyone. Companies lose hours every day just from employees forgetting and resetting passwords. A single data breach costs millions of dollars. 1Password secures every sign-in to save you time and money.
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Explore the world of AI development with Will Falcon, creator of PyTorch Lightning and founder of Lightning AI.
In this episode of Eye on AI, Will takes us through the groundbreaking journey of Lightning Studio, a revolutionary cloud-based platform that's reshaping how AI tools and frameworks are integrated and scaled. From his unique start in the US military to leading AI innovation, Will offers a comprehensive look at the evolution of machine learning technologies that simplify and accelerate the deployment of AI models.
The discussion delves into the creation of PyTorch Lightning, highlighting its role in democratizing AI development by structuring code and enabling scalability across thousands of machines. Will also outlines the pivotal shift from traditional coding practices to a streamlined, cloud-based environment, making advanced AI tools accessible to a global community of developers.
Tune in as we uncover the layers of technology that drive AI forward and discuss the future of AI development platforms.
Will's insights are crucial for anyone interested in the intersection of AI, software engineering, and cloud infrastructure.
Make sure to hit the like button and subscribe for more insights into the technologies shaping our world.
This episode is sponsored by Oracle. AI is revolutionizing industries, but needs power without breaking the bank. Enter Oracle Cloud Infrastructure (OCI): the one-stop platform for all your AI needs, with 4-8x the bandwidth of other clouds. Train AI models faster and at half the cost. Be ahead like Uber and Cohere.
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Navigating AI Frontiers: William Falcon Explores PyTorch Lightning's Evolution
In this episode, we navigate the frontiers of AI alongside William Falcon, CEO of Lightning AI, as he delves into the evolutionary journey of PyTorch Lightning.
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767: Open-Source LLM Libraries and Techniques, with Dr. Sebastian Raschka
Jon Krohn sits down with Sebastian Raschka to discuss his latest book, Machine Learning Q and AI, the open-source libraries developed by Lightning AI, how to exploit the greatest opportunities for LLM development, and what’s on the horizon for LLMs.
This episode is brought to you by the DataConnect Conference, and by Data Universe, the out-of-this-world data conference. Interested in sponsoring a SuperDataScience Podcast episode? Visit passionfroot.me/superdatascience for sponsorship information.
In this episode you will learn:
• All about Machine Learning Q and AI [04:13]
• Sebastian Raschka’s role as Staff Research Engineer at Lightning AI [19:21]
• PyTorch Lightning’s and Lightning Fabric’s capabilities [39:32]
• Large language models: Opportunities and challenges [43:35]
• DoRA vs LoRA [48:56]
• How to be a successful AI educator [1:34:18]
Additional materials: www.superdatascience.com/767
The creator of PyTorch Lightning on the AI hype cycle
William is the CEO of Lightning AI and the creator of PyTorch Lightning, the lightweight PyTorch wrapper for high-performance AI research.
Dive into their docs or explore the developer community.
ICYMI: Across tech, layoffs are boosting share prices.
Follow William on Twitter or connect with him on LinkedIn.
Shoutout to Brian61354270, who earned a Lifeboat badge by answering ModuleNotFoundError: No module named 'distutils' in Python 3.12.
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Will Falcon — Making Lightning the Apple of ML
Will Falcon is the CEO and co-founder of Lightning AI, a platform that enables users to quickly build and publish ML models.
In this episode, Will explains how Lightning addresses the challenges of a fragmented AI ecosystem and reveals which framework PyTorch Lightning was originally built upon (hint: not PyTorch!) He also shares lessons he took from his experience serving in the military and offers a recommendation to veterans who want to work in tech.
Show notes (transcript and links): http://wandb.me/gd-will-falcon
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⏳ Timestamps:
00:00 Intro
01:00 From SEAL training to FAIR
04:17 Stress-testing Lightning
07:55 Choosing PyTorch over TensorFlow and other frameworks
13:16 Components of the Lightning platform
17:01 Launching Lightning from Facebook
19:09 Similarities between leadership and research
22:08 Lessons from the military
26:56 Scaling PyTorch Lightning to Lightning AI
33:21 Hiring the right people
35:21 The future of Lightning
39:53 Reducing algorithm complexity in self-supervised learning
42:19 A fragmented ML landscape
44:35 Outro
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Connect with Lightning
📍 Website: https://lightning.ai
📍 Twitter: https://twitter.com/LightningAI
📍 LinkedIn: https://www.linkedin.com/company/pytorch-lightning/
📍 Careers: https://boards.greenhouse.io/lightningai
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💬 Host: Lukas Biewald
📹 Producers: Riley Fields, Anish Shah, Cayla Sharp, Angelica Pan, Lavanya Shukla
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Advancing Hands-On Machine Learning Education with Sebastian Raschka - #565
Today we’re joined by Sebastian Raschka, an assistant professor at the University of Wisconsin-Madison and lead AI educator at Grid.ai. In our conversation with Sebastian, we explore his work around AI education, including the “hands-on” philosophy that he takes when building these courses, his recent book Machine Learning with PyTorch and Scikit-Learn, his advise to beginners in the field when they’re trying to choose tools and frameworks, and more.
We also discuss his work on Pytorch Lightning, a platform that allows users to organize their code and integrate it into other technologies, before switching gears and discuss his recent research efforts around ordinal regression, including a ton of great references that we’ll link on the show notes page below!
The complete show notes for this episode can be found at twimlai.com/go/565
Multi-GPU training is hard (without PyTorch Lightning)
William Falcon wants AI practitioners to spend more time on model development, and less time on engineering. PyTorch Lightning is a lightweight PyTorch wrapper for high-performance AI research that lets you train on multiple-GPUs, TPUs, CPUs and even in 16-bit precision without changing your code! In this episode, we dig deep into Lightning, how it works, and what it is enabling. William also discusses the Grid AI platform (built on top of PyTorch Lightning). This platform lets you seamlessly train 100s of Machine Learning models on the cloud from your laptop.
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Featuring:
William Falcon – Website, GitHub, X
Chris Benson – Website, GitHub, LinkedIn, X
Daniel Whitenack – Website, GitHub, X
Show Notes:
PyTorch Lightning | Website
PyTorch Lightning | Twitter
PyTorch Lightning | LinkedIn
PyTorch Lightning | GitHub
Grid.ai | Website
Grid.ai | Twitter
Grid.ai | LinkedIn
Grid AI, From the Makers of PyTorch Lightning, Emerges From Stealth With $18.6m Series A to Close the Gap Between AI Research and Production
Grid AI raises $18.6M Series A to help AI researchers and engineers bring their models to production
Grid.ai Launches Platform to Train Machine Learning Models in the Cloud
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