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How AI will change software engineering – with Martin Fowler

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Nov 19, 2025

Guest

Martin Fowler

In a compelling discussion, Martin Fowler, Chief Scientist at ThoughtWorks and renowned author, delves into the transformative impact of AI on software engineering. He compares AI's significance to the shift from assembly to high-level languages, highlighting how generative models aid in prototyping and understanding legacy code. Fowler emphasizes the importance of rigorous testing for LLM outputs and cautions against the potential pitfalls of 'vibe coding.' He also underlines the continuing relevance of refactoring in the age of AI, advocating for a balanced approach in leveraging these new tools.

01:48:53

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insights

INSIGHT

Determinism To Non-Determinism Shift

The big shift with AI is from deterministic systems to non-deterministic ones.
That change forces engineers to think in terms of tolerances and verification, not absolute certainty.

insights

INSIGHT

Co-Build Abstractions With LLMs Carefully

LLMs help build abstractions but introduce non-deterministic implementations of them.
Combining LLMs with rigorous domain languages improves precision and control.

insights

INSIGHT

LLMs For Legacy Understanding

LLMs excel at helping understand legacy systems by building semantic graphs and querying them.
ThoughtWorks moved that practice into 'adopt' because it materially speeds modernization work.

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How Martin Fowler got into software

01:44 • 2min

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Early jobs and object orientation

03:38 • 2min

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From independent consultant to ThoughtWorks

05:30 • 4min

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Role as ThoughtWorks Chief Scientist

09:04 • 1min

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How the Technology Radar is created

10:27 • 6min

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AI as the biggest shift in software

16:38 • 4min

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Abstraction vs non-determinism with LLMs

21:08 • 4min

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Where LLMs help: prototyping and legacy code

24:56 • 5min

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Working with LLMs in teams

30:13 • 4min

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Vibe coding's limits and lost learning

33:49 • 4min

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Making LLMs part of learning and tooling

Tests and verification with AI

43:23 • 2min

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Practical LLM uses at ThoughtWorks

45:48 • 3min

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Spec-driven ideas and DSLs with LLMs

49:00 • 2min

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Enterprise constraints and risk tolerance

50:41 • 6min

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Refactoring's continuing importance

56:40 • 9min

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Deterministic tools combined with LLMs

01:05:10 • 2min

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Patterns, architecture vocabulary, and cloud effects

01:07:31 • 8min

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Complexity of large, long-lived systems

01:15:52 • 2min

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Origins and impact of the Agile Manifesto

01:18:17 • 6min

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Agile, AI, and the value of faster cycles

01:24:42 • 4min

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How Martin keeps learning about AI

01:28:29 • 6min

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Advice for juniors using AI

01:34:24 • 3min

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Outlook on the tech industry and AI

01:37:38 • 5min

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Favorites: languages, books, and games

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Patterns of Enterprise Application Architecture

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Refactoring

Improving the Design of Existing Code

Martin Fowler

This book, now in its second edition, provides a detailed catalog of refactoring techniques. It explains how to identify 'code smells' and apply behavior-preserving transformations to improve the design and maintainability of software. The book includes examples in Java and JavaScript, and it emphasizes the importance of testing and small, incremental changes to minimize the risk of introducing errors. The second edition reflects the changes in the programming landscape over the past two decades and includes new examples and techniques.

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Martin Fowler is one of the most influential people within software architecture, and the broader tech industry. He is the Chief Scientist at Thoughtworks and the author of Refactoring and Patterns of Enterprise Application Architecture, and several other books. He has spent decades shaping how engineers think about design, architecture, and process, and regularly publishes on his blog, MartinFowler.com.

In this episode, we discuss how AI is changing software development: the shift from deterministic to non-deterministic coding; where generative models help with legacy code; and the narrow but useful cases for vibe coding. Martin explains why LLM output must be tested rigorously, why refactoring is more important than ever, and how combining AI tools with deterministic techniques may be what engineering teams need.

We also revisit the origins of the Agile Manifesto and talk about why, despite rapid changes in tooling and workflows, the skills that make a great engineer remain largely unchanged.

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Timestamps

(00:00) Intro

(01:50) How Martin got into software engineering

(07:48) Joining Thoughtworks

(10:07) The Thoughtworks Technology Radar

(16:45) From Assembly to high-level languages

(25:08) Non-determinism

(33:38) Vibe coding

(39:22) StackOverflow vs. coding with AI

(43:25) Importance of testing with LLMs

(50:45) LLMs for enterprise software