Beyond the Junior Developer Debate: AI’s True Impact on Engineering Teams

The following is an excerpt from the forthcoming book Vibe Coding: Building Production-Grade Software With GenAI, Chat, Agents, and Beyond by Gene Kim and Steve Yegge.

Traditional professional kitchens have a clear hierarchy: head chefs design the menu and oversee operations, experienced line cooks handle complex dishes, and new apprentices learn by starting with simple tasks like chopping vegetables and washing dishes.

For decades, we’ve organized software engineering teams in the same way: senior principal engineers design project architecture, mid-level engineers build complex features, and junior developers learn by handling small, contained tasks. This hierarchy shaped how we hired, trained, and promoted engineers. It’s how most of us learned the ropes.

AI, being super fast, changes everything. Let’s visualize this using a “task tree.” Big company goals form the trunk, branching into major features, which then sprout smaller branches and finally leaves—individual functions, tests, documentation bits. Historically, those leaf nodes were the proving ground for junior talent.

Many have noted that AIs excel at these leaf-node tasks. Tasks that once took a junior developer days might now be handled in hours by a senior engineer guiding an AI assistant. Steve’s Head of AI trained and deployed a machine learning model in an afternoon. Had it been done the previous year, it would have been a two-month summer intern project. This observation partly inspired Steve’s June 2024 “Death of the Junior Developer” post. In the FAAFO model, senior engineers can do things faster and more autonomously, which (we thought at the time) cuts the junior Devs out.

But the reality is more nuanced and, frankly, more interesting than a simple replacement story. Unlike what we thought, everyone in the organization will be using AI.

Junior developers will not become redundant. Far from it. Their role is evolving. Instead of primarily executing leaf tasks, they might become the “station leads” of the kitchen, who help integrate contributions from non-engineers across the company. We’re seeing a fascinating trend where people outside traditional engineering roles—UX designers, product managers, infrastructure Ops—use AI to contribute directly to the code base. A junior engineer, like a junior doctor, is still highly trained and can be super valuable in helping this new generation of budding “field medics” contribute directly to the code.

Software delivery is evolving into a vibrant ecosystem, where all roles are now contributing to the code. One UX designer we know, Daniel, was frustrated by a missing feature and built it himself (along with tests) with AI’s help, impressing the engineering team. 

We hear more and more stories like Daniel’s. We believe junior developers will increasingly work with these creative professionals and knowledge workers, including helping them and integrating their work, because most of it would have been done by junior Devs in the past. This makes them a good resource for helping less technical people perform that work.

Vibe coding is starting to happen everywhere in the organization where people are waiting for developers or engineers. In the past, these people were either stuck, had to use outside vendors, or had to escalate up the hierarchy. Now, they can create the software themselves—building prototypes, fixing issues, and maybe building features (or at least starting them).

Senior engineers will become responsible for more because what can be accomplished will be greater (ambitious), and they’ll be responsible for the contributions of many people, all armed with AI.

With the vision we see unfolding of all knowledge workers beginning to vibe code, engineers still have important roles, though they will be different. Offering a pragmatic perspective amid these shifting roles, Dave Cohen, VP of Engineering at UTR Sports (and a former engineering leader at Facebook and Google), gives advice we all should find heartening: 

Don’t worry, engineers—the current generation of AI tools won’t replace you anytime soon…

There Will Be More Developer Jobs, Not Fewer

We talked with Tim O’Reilly recently, who invented the term “Web 2.0” and is famous for his publishing empire, which has taught us many essential skills. We got onto the topic of AI coding, and he reminded us that we’ve seen this movie before. Every single time we’ve had a significant leap in programming technology; people predict the programmer apocalypse:

“High-level languages will kill assembly programmers!”

“Visual Basic will replace professional developers!”

“Low-code platforms will make developers obsolete!”

“No-code tools mean the end of software engineering!”

However, each time programming got easier we needed more programmers. Easier tools meant more people could build software, which created new categories of applications, which spawned new industries, which required…you guessed it…more developers.

Look at what happened with the web. HTML was dead simple compared to C++. Everyone and their grandmother could make a webpage. It did the opposite of killing programming jobs. It exploded the demand for software, creating millions of new programming jobs across countless new businesses.

Dr. Matt Beane, author of The Skill Code and famous for his work on studying the “novice optional problem,” speculated on the variety of new roles that could emerge in the software creation process. (We talk more about his prediction of what new software roles might get created in Part 4, based on his study of the latest roles that were created in fulfillment centers as more work was automated.)

Furthermore, existing roles will all become enhanced with AI. A security engineer is still a security engineer, for instance, but they will be using AI to automate a lot of the job. Security engineers have always wanted to implement fixes directly in the code, but it’s not always feasible for them to know every language and framework at the company. With AI, they can confidently make security fixes and add defenses across the company’s code, provided the work is reviewed by an appropriately leveled engineer.

This pattern of AI role augmentation starts to capture Scott Belsky’s notion of “collapsing the stack” we mentioned earlier—where Daniel the UX designer is proving that he, too, can be an engineer, and he can start to work his way up in engineering experience by building software with his own hands. Likewise, professional engineers no longer need to wait on or be blocked by UX designers; engineers can take on many UX responsibilities in less user-critical scenarios.

The UX designer role seems to be broadening—a UX++ role that straddles the line between designer and engineer. Daniel gives us a glimpse of a world where UX specialists implement the UX layer themselves rather than relying on developers. In this new world, people will vastly prefer working with UX designers who participate in development rather than sitting on the sidelines in Figma, opening tickets for developers to resize panes and move buttons.

So, what does this mean for jobs, precisely? Will everyone need to learn to code? Let’s study a comparable situation that unfolded with photography and see if we can learn anything from it.

When digital cameras first appeared, professional photographers scoffed, convinced that mastering f-stops, lighting, and film chemistry was the only real path to capturing great images. Yet over the following decade, an unexpected shift occurred: digital photography didn’t shutter the profession—it blew open the doors. Suddenly, anyone with a smartphone was an amateur photographer, creating billions more photographs. This explosion in photography birthed new industries—social media influencers, image-sharing networks, online portfolios—and dramatically expanded the overall demand for professional imagery.

The same dynamic will likely unfold with software creation. As vibe coding tools become increasingly intuitive and widespread—and eventually, as easy to use as smartphones—software development moves from a specialized discipline accessible only to highly trained engineers, toward something anyone with a good idea can go after. 

We’ve already seen teenage vibe coders building robust gaming apps—something once reserved for industry veterans. In this environment, software will become as ubiquitous as photos and videos, an everyday medium for communication, collaboration, and creativity.

As you might still hire a professional photographer for demanding shoots, there will always be a critical need for highly skilled software engineers in areas that demand exceptional resilience, security, and enterprise-level scalability. (Say, software for airplanes or CT scanners.)

Get ready for a world where software becomes another form of creative expression, and where the millions of little features that someone needs, languishing in a bug backlog, can be built and implemented by anyone.

Our math here is simple and optimistic: When you lower barriers, more people create stuff. And those creations—whether digital photos or software apps—create new markets, opportunities, and yes, more jobs.

Stay tuned for more exclusive excerpts from the upcoming book Vibe Coding: Building Production-Grade Software With GenAI, Chat, Agents, and Beyond by Gene Kim and Steve Yegge on this blog or by signing up for the IT Revolution newsletter.

- About The Authors

Gene Kim

Gene Kim has been studying high-performing technology organizations since 1999. He was the founder and CTO of Tripwire, Inc., an enterprise security software company, where he served for 13 years. His books have sold over 1 million copies—he is the WSJ bestselling author of Wiring the Winning Organization, The Unicorn Project, and co-author of The Phoenix Project, The DevOps Handbook, and the Shingo Publication Award-winning Accelerate. Since 2014, he has been the organizer of DevOps Enterprise Summit (now Enterprise Technology Leadership Summit), studying the technology transformations of large, complex organizations.

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Steve Yegge

Steve Yegge is an American computer programmer and blogger known for writing about programming languages, productivity, and software culture for two decades. He has spent over thirty years in the industry, split evenly between dev and leadership roles, including nineteen years combined at Google and Amazon. Steve has written over a million lines of production code in a dozen languages, has helped build and launch many large production systems at big tech companies, has led multiple teams of up to 150 people, and has spent much of his career relentlessly focused on making himself and other developers faster and better. He is currently an Engineer at Sourcegraph working on AI coding assistants.

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