Episode
13

Tim O'Reilly on The End of Programming As We Know It

Tim O’Reilly—founder of O’Reilly Media and one of the most influential voices in tech—argues we’re not witnessing the end of programming, but the beginning of something far bigger. He draws on past computing revolutions to explore how AI is reshaping what it means to build software, why real breakthroughs come from the edge—not incumbents—and what it takes to learn, teach, and build responsibly in the age of AI.
March 27, 2025
Listen on
spotify logoApple podcast logo
Guest
Tim O'Reilly

O'Reilly Media

,
Tim O’Reilly is the Founder and CEO of O’Reilly Media, a company that has shaped the modern tech landscape by publishing books and hosting conferences that champion emerging technologies. Through his early work promoting the internet, Perl, Linux, open source, XML, and web services, Tim and his company helped bring foundational technologies into the mainstream. He’s known for coining influential terms like “Web 2.0” and for his unique ability to identify and amplify the work of forward-thinking technologists—what he calls “watching the alpha geeks.” Beyond publishing, Tim has expanded into online media, event programming, and early-stage venture investing. He serves on the boards of organizations including Code for America, Maker Media, PeerJ, Popvox, Safari Books Online, and Civis Analytics. Throughout his career, Tim has played a pivotal role in capturing and spreading the knowledge of innovators to help shape the future of technology.
HOST
Hugo Bowne-Anderson

Delphina

Hugo Bowne-Anderson is an independent data and AI consultant with extensive experience in the tech industry. He is the host of the industry podcast Vanishing Gradients, a podcast exploring developments in data science and AI. Previously, Hugo served as Head of Developer Relations at Outerbounds and held roles at Coiled and DataCamp, where his work in data science education reached over 3 million learners. He has taught at Yale University, Cold Spring Harbor Laboratory, and conferences like SciPy and PyCon, and is a passionate advocate for democratizing data skills and open-source tools.

‍

‍

‍

‍

‍

‍

Key Quotes

Key Takeaways

‍

📚 Programming isn’t ending—it’s evolving.

Tim compares today’s AI-driven shift to past revolutions like compilers and interpreters. Each abstraction layer widened access and creativity. AI is the latest layer—expanding who can build, not eliminating the need to build.

‍

🔧 AI makes more things buildable by more people.

From high schoolers prototyping medical tools to solo creators making films, Tim emphasizes that democratization—not displacement—is the dominant trend.

‍

🕸 The real breakthroughs won’t come from the center.

Just like Google wasn’t AOL and Amazon wasn’t Barnes & Noble, the next AI era will be shaped by decentralized players. Tim points to efforts like DeepSeek as early signals of where true innovation is happening.

‍

🏗 We need a new architecture of participation.

Inspired by UNIX and the open web, Tim calls for a modular, interoperable AI ecosystem—an “architecture of participation” where AIs cooperate, negotiate, and share knowledge, rather than compete behind closed APIs.

‍

🛠 The future of software engineering is systems thinking.

LLMs won’t erase the need for engineering—they’ll change the job. From “vibe-driven coding” to debugging AI agents, tomorrow’s engineers will orchestrate systems, define interfaces, and evaluate outcomes.

‍

📊 Organizations must become learning machines.

Ambient learning isn’t optional. Tim urges teams to share what’s working, experiment in public, and treat learning as a continuous strategic asset—not an afterthought.

‍

🧭 Think in vectors, not snapshots.

Use directional trends to shape strategy—watch for acceleration, not just dominance. Tim urges leaders to track where things are going, not just where they are.

‍

You can read the full transcript here.

‍

00:00 The Evolution of Programming: From Compilers to AI

01:49 Introducing Tim O'Reilly: A Visionary in Technology

02:19 AI's Impact on Programming and Development

07:15 The Democratization of Technology and Creativity

15:02 The Future of AI and Decentralization

26:48 The Changing Landscape of Software Development

39:40 The Commoditization of Power and Recycled Intelligence

40:11 The Evolution of O'Reilly and Education

41:07 Teaching Generative AI and Overcoming Learning Hurdles

42:18 The Unix Experience and Learning by Doing

43:41 AI as a Tool for Non-Programmers

45:25 The Fragility of Software and Technical Debt

46:40 The Importance of Passion Projects in Learning

48:36 Organizing Time for Learning in Chaotic Environments

53:34 The Future of AI: Decentralization and Architecture of Participation

01:02:26 AI Governance and Communication Protocols

01:05:00 The Role of Cloud Infrastructure in AI Safety

01:17:40 The Future of Jobs in an AI-Driven World

01:21:43 Final Thoughts and Practical Advice

‍

‍

‍

Links From The Show

‍

Transcript

featured

In the spotlight: Our most popular episodes

Episode
10

Ari Kaplan on Why AI Won't Save You But Data Intelligence Will

Ari Kaplan—Global Head of Evangelism at Databricks and a pioneer in sports analytics—explains why businesses fixated on AI often overlook the real advantage: making better decisions with their own data. He shares lessons from his work building analytics teams for Major League Baseball, advising McLaren’s F1 strategy, and helping companies apply AI where it actually works—without falling into hype-driven traps.

With
Ari Kaplan
Global Head of Evangelism
,
Databricks
Play episode
Episode
5

Gabriel Weintraub on The Hard Truth About Building AI Systems and What Most Leaders Miss About AI

In this episode of High Signal, Gabriel Weintraub (the Amman Professor of Operations, Information, and Technology at Stanford Graduate School of Business), brings his expertise in market design, data science, and operations, enriched by his experience with global platforms like Uber and Mercado Libre, to a conversation that spans practical strategies, cultural insights, and global perspectives on data and AI.
With
Gabriel Weintraub
Amman Mineral Professor of Operations, Information & Technology
,
Stanford Graduate School of Business
Episode
3

Chiara Farronato on Data Science Meets Management: Teamwork, Experimentation, and Decision-Making

Harvard Business School's Chiara Farronato discusses how digital platforms like Airbnb and Uber have transformed industries. She explores the challenges of fostering collaboration between managers and data scientists, bridging communication gaps, and building data-driven cultures. Chiara also delves into the complexities of managing peer-to-peer marketplaces and the evolving role of data in decision-making. This episode offers key insights for business leaders working with technical teams and navigating platform-based innovation.
With
Chiara Farronato
Associate Professor
,
Harvard Business School
most recent

Listen up: Our latest discussions

Hear the hottest takes on data science and AI.

See all episodes
Episode
14

Barr Moses on Why Most Companies Aren’t Actually AI Ready (and What to Do About It)

Barr Moses—co-founder and CEO of Monte Carlo—thinks we’re headed for an AI reckoning. Companies are building fast, but most are still managing data like it’s 2015. In this episode, she shares high-stakes failure stories (like a $100M schema change), explains why full-stack observability is becoming essential, and breaks down how LLM agents are already transforming data debugging. From culture to tooling, this is a sharp look at what real AI readiness requires—and why so few teams have it.
With
Barr Moses
CEO and Co-Founder
,
Monte Carlo
Episode
13

Tim O'Reilly on The End of Programming As We Know It

Tim O’Reilly—founder of O’Reilly Media and one of the most influential voices in tech—argues we’re not witnessing the end of programming, but the beginning of something far bigger. He draws on past computing revolutions to explore how AI is reshaping what it means to build software, why real breakthroughs come from the edge—not incumbents—and what it takes to learn, teach, and build responsibly in the age of AI.
With
Tim O'Reilly
Founder, Chairman, and CEO
,
O'Reilly Media
Episode
12

Stefan Wager on Why Your Machine Learning Solves The Wrong Problem

Stefan Wager—Professor at Stanford and expert on causal machine learning—has worked with leading tech companies including Dropbox, Facebook, Google, and Uber. He challenges the widespread assumption that better predictions mean better decisions. Traditional machine learning excels at prediction, but is prediction really what your business needs? Stefan explores why predictive models alone often fail to answer critical “what-if” questions, how causal machine learning bridges this gap, and provides practical advice for how you can start applying causal ML at work.
With
Stefan Wager
Associate professor of operations, information and technology at Stanford Graduate School of Business
,
Stanford University

Get the latest episodes in your inbox

Never miss an episode of High Signal by signing up for the Delphina newsletter.

By clicking Sign Up you're confirming that you agree with our Terms and Conditions.
Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.