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Estimated reading time: 5 minutes
It’s clear that the introduction of AI coding assistants is impacting the way engineers not only do their jobs, but how they feel about it.
While coding assistants have already been widely adopted by engineers, the results can be somewhat mixed. Further, it’s still unclear how these changes will impact the role of software engineers and traditional team structures. This goes beyond just fears of job losses, which are justified, toward how organizations will better understand effectiveness when the most basic engineering tasks can be handed off to a machine.
Here are some key perspectives on this topic, which should help engineering leaders understand this shift and what they might have to do to adapt to it.
The Software Engineering Identity Crisis by Annie Vella
Distinguished engineer Annie Vella really landed on something with this piece about the looming identity crisis many engineers will be facing as AI tools take on the very tasks they spent their careers, or even lives, mastering.
My favourite part: “With humans, trust grows gradually, through shared success. Each problem solved together strengthens the bond. Even failures can deepen trust when handled well. With AI, trust often starts high and erodes fast.”
The End of Programming as We Know It by Tim O’Reilly
He’s ridden plenty of these waves already, so Tim O’Reilly’s worth reading on the topic. Here, he lays out a pretty optimistic view of what generative AI means for the future of programming as a discipline, placing everything in a useful historical context.
My favourite part: “This was far from the end of programming, though. There were more programmers than ever. Users in the hundreds of millions consumed the fruits of their creativity. In a classic demonstration of elasticity of demand, as software was easier to create, its price fell, allowing developers to create solutions that more people were willing to pay for.”
Career advice in 2025 by Will Larson
There’s no sugar coating in this Will Larson post. He offers some important career advice for navigating an already challenging job market in a world where large language models (LLMs) become a “core product and development tool.”
My favourite part: “Sitting out this transition, when we are relearning how to develop software, feels like a high risk proposition. Your well-honed skills in team development are already devalued today relative to three years ago, and now your other skills are at risk of being devalued as well.”
RDEL #85: What is the impact of generative AI on software development? by Lizzie Matusov
Lizzie Matusov’s research-backed engineering leadership newsletter highlights data that can tell us something about the state of the industry. In this edition, she digs into the 2024 DORA report, which focuses on whether the introduction of AI coding tools are helping teams be more effective, or are just displacing toil into different parts of the software development lifecycle.
My favourite part: “Speed gains in one area can create bottlenecks in another—especially if fundamentals like batch size, testing discipline, or team coordination don’t keep pace.”
AI Engineering in the real world by Gergely Orosz
In this edition of the Pragmatic Engineer newsletter, Orosz shines a light on how some engineers got ahead of the curve and built AI into real, customer-facing applications, with a bunch of common challenges and lessons learned to take away.
My favourite part: “AI engineering is an exciting, rapidly evolving field. My sense is that working with LLMs will become a baseline expectation for most software engineers at startups and scaleups, similar to how being on the on-call rotation is a given for engineers at many companies.”
Revenge of the junior developer by Steve Yegge
Yegge has produced a host of eloquent and vocal articles on AI coding assistants hitting the mainstream. This post, which followed last year’s the death of the junior developer, definitely hit a nerve, especially if you’re later into your career and have a penchant for yelling at clouds.
My favourite part: “Junior devs are vibing. They get it. The world is changing, and you have to adapt. So they adapt!”
Leading Effective Engineering Teams in the Age of GenAI by Addy Osmani
This is a nice refresher of Osmani’s principles for leading engineering teams, but put into the context of the AI era, including nine handy principles for leaders.
My favourite part: “Shift from direct code monitoring to strategic guidance, focusing on ensuring proper AI usage and output quality.”
More like this
How Engineering Teams are Using AI by Luca Rossi
In this edition of his Refactoring newsletter, Rossi focuses on how the Augment coding assistant highlights a growing divide between the value small vs large companies, and individuals vs teams are able to squeeze out of tools like these.
My favourite part: ”Green-field vs big codebases — most tools out there are optimized for green-field development. And so are most of the examples you can find. Real teams with grizzled codebases can’t do much with e.g. Lovable.”
Not all AI-assisted programming is vibe coding (but vibe coding rocks) by Simon Willison
Simon Willison has been exemplary at learning in public when it comes to LLMs and all of the coding tools they have spawned over the past few years. In this post, he digs into the confusion many already have around the term vibe coding, its potential to democratize software development, and why it’s still a valuable approach for developers.
My favourite part: “I don’t want ‘vibe coding’ to become a negative term that’s synonymous with irresponsible AI-assisted programming either. This weird new shape of programming has so much to offer the world!”
London • June 16 & 17, 2025
Speakers Gergely Orosz, Camille Fournier and Lara Hogan confirmed.
Final thoughts
This list of articles will probably go out of date before publishing, but there are some key themes emerging for engineering leaders to keep an eye on:
- The looming developer identity crisis
- Setting effective policy and guardrails around AI tool use within engineering teams
- Effectively building AI into products (AI engineering)
- Skills atrophy and the impact on junior developers
- Vibe coding and simpler prototyping to speed up innovation
- Durable engineering and management skills in an age of AI
- The importance of adaptability, don’t be the stubborn developer
Industry leaders will really reckon with a disruption to how they operate in an effort to ride this wave of change as effectively as possible. There will always be optimistic early adopters and stubborn holdouts, but there is no denying that a major shift has arrived.
