On this episode of Learning from Machine Learning, I had the pleasure of speaking with Dan Bricklin, co-creator of VisiCalc - the first electronic spreadsheet and the killer app that launched the personal computer revolution. We explored what five decades of platform shifts teach us about today's AI moment.

Dan's framework is simple but powerful: breakthrough innovations must be 100 times better, not incrementally better. The same questions he asked about spreadsheets apply to AI today: What is this genuinely better at? What does it enable? What trade-offs will people accept? Does it pay for itself immediately?

Most importantly, Dan reminded us that we never fully know the impact of what we build. Whether it's a mother whose daughter with cerebral palsy can finally do her own homework, or a couple who met learning spreadsheets. The moments worth remembering aren't the product launches or exits. They're the unexpected times when your work changes someone's life in ways you never imagined.

Substack

Youtube

---

Chapters

00:00:00 Start

00:00:49 Early Fascination with Technology

00:02:49 From MIT to Mainframes: A Journey Through Computing

00:09:35 The Birth of VisiCalc: Revolutionizing Spreadsheets

00:13:41 Interactive Computing: The Impact of VisiCalc

00:16:46 Understanding Killer Apps: The Evolution of Software

00:23:05 Challenges in Development: Creating VisiCalc

00:30:12 VisiCalc's Legacy: The Precursor to Modern Spreadsheets

00:36:01 App Customization

00:40:31 The Evolution of User Interfaces and Applications

00:43:11 Understanding AI: Hype vs. Reality

00:48:11 Learning to Use New Technologies Effectively

00:56:51 The Importance of User-Centric Design

01:01:10 Career Reflections and Life Lessons

01:05:18 The Impact of Technology on Life and Relationships

---

A machine learning podcast that explores more than just algorithms and data: Life lessons from the experts. Welcome to "Learning from Machine Learning," a podcast about the insights gained from a career in the field of Machine Learning and Data Science. In each episode, industry experts, entrepreneurs and practitioners will share their experiences and advice on what it takes to succeed in this rapidly-evolving field.

---

• Resources to learn more about Learning from Machine Learning
  • https://www.linkedin.com/company/learning-from-machine-learning
  • https://www.linkedin.com/in/sethplevine/
  • https://medium.com/@levine.seth.p
  • https://mindfulmachines.substack.com/

On this episode of Learning from Machine Learning, I had the privilege of speaking with Lukas Biewald, co-founder and CEO of Weights & Biases. We traced his journey from programming games as a kid to building one of the most essential tools in AI development today. Lukas's career demonstrates that conviction often matters more than consensus—from surviving the AI winter in the mid-2000s when he was coached to remove "AI" from investor pitches, to the AlphaGo moment that changed everything and led him to take an unpaid internship at OpenAI in his thirties.

Lukas's philosophy on "automating the automation" reveals why AI developers have become the most powerful people within organizations—they're a smaller market but wield disproportionate influence. He shares his view that "if you zoom out, AI is so underhyped, you can't hype it enough." The recursive potential of machines improving machines is barely understood, yet it represents "the most powerful technology you could possibly build."

Most importantly, Lukas's philosophy that "feedback loops are your units of work" transforms how we approach both machine learning and life. He explains the necessity to stay technical as a leader: "If you're going to work for me, you better be able to do the IC job. And I do not know how companies function without that mindset." His advice to his younger self cuts through common doubts in emerging technologies: "you think you're late, but you're early." In a world racing towards progress at all costs, this reminder couldn't be more relevant.

Thank you for listening. Be sure to subscribe and share with a friend or colleague.

---

Available on all podcast platforms:

https://rss.com/podcasts/learning-from-machine-learning/

Available on Youtube:

https://www.youtube.com/@learningfrommachinelearning

Available on Substack:

https://mindfulmachines.substack.com/

---

Chapters

00:00 Open

00:46 Early Fascination with AI

03:57 Founding CrowdFlower During AI Winter

09:22 The AlphaGo Awakening

16:02 Birth of Weights & Biases

23:50 The LLM Revolution's Impact

29:12 CoreWeave Acquisition & Future Vision

32:56 The Entrepreneurship Philosophy

37:29 Technical Leadership Philosophy

49:01 The Future of Software Development

53:07 Leadership Lessons & Career Advice

1:00:38 Life Lessons from Machine Learning

1:01:46 Closing Thoughts & Gratitude

---

References

• Gödel, Escher, Bach: An Eternal Golden Braid
• Genius Makers
• Weights & Biases
• CrowdFlower/Figure 8 (now part of Appen)
• OpenAI
• CoreWeave
• Scale AI
• GitHub
• Google
• Stanford University
• Y Combinator
• Daphne Koller - Stanford Professor, Co-founder of Coursera
• Lee Sedol - Professional Go player defeated by AlphaGo

---

A machine learning podcast that explores more than just algorithms and data: Life lessons from the experts. Welcome to "Learning from Machine Learning," a podcast about the insights gained from a career in the field of Machine Learning and Data Science. In each episode, industry experts, entrepreneurs and practitioners will share their experiences and advice on what it takes to succeed in this rapidly-evolving field.

On this episode of Learning from Machine Learning, I had the privilege of speaking with Maxime Labonne, Head of Post-Training at Liquid AI. We traced his journey from cybersecurity to the cutting edge of model architecture. Maxime shared how the future of AI isn't just about making models bigger—it's about making them smarter and more efficient.

Maxime's work demonstrates that challenging established paradigms requires taking steps backward to leap forward. His framework for data quality—accuracy, diversity, and complexity—offers a blueprint for anyone working with machine learning systems.

Most importantly, Maxime's perspective on learning itself—treating knowledge acquisition like training data exposure—reminds us that growth comes from diverse, high-quality experiences across different contexts. Whether you're training a model or developing yourself, the principles remain remarkably similar.

Thank you for listening. Be sure to subscribe and share with a friend or colleague. Until next time... keep on learning.

00:46 Introduction and Maxime's Background

01:47 Journey from Cybersecurity to Machine Learning

03:30 The Fascination with AI and Cyber Attacks

06:15 Transitioning to Post-Training at Liquid AI

08:17 Liquid AI's Vision and Mission

10:08 Challenges of Deploying AI on Edge Devices

13:06 Techniques for Efficient Edge Model Training

15:44 The State of AI Hype and Reality

19:19 Evaluating AI Models and Benchmarks

24:09 Future of AI Architectures Beyond Transformers

31:05 Innovations in Model Architecture

36:28 The Importance of Iteration in AI Development

39:24 Understanding State Space Models

42:53 Advice for Aspiring Machine Learning Professionals

48:53 The Quest for Quality Data

52:56 Integrating User Feedback into AI Systems

58:13 Lessons from Machine Learning for Life