From Coding to Steering: The Developer's New Role in the AI Era

The rise of AI-assisted development isn't just changing how we write code—it's fundamentally redefining what it means to be a software developer.

The Writing on the Wall

When Boris Cherny, head of Claude Code at Anthropic, announced that he hadn't written a single line of code in over two months—shipping 22 pull requests in a day, all 100% AI-generated—it wasn't just a provocative tweet. It was a glimpse into a future that's arriving faster than most teams are prepared to admit.

The question isn't whether AI will transform software development. That transformation is already underway. The real question is: what happens to developers when the actual coding becomes a commodity?

The Last Time This Happened

We've been here before. Not exactly, but close enough to recognize the pattern.

In the 1970s and early 80s, assembly language programmers were the elite. They understood the machine at its deepest level, could optimize every instruction, squeeze performance from limited resources. Then came higher-level languages—C, Pascal, and later Java, Python, JavaScript.

Assembly programmers protested: "These abstractions are too slow, too wasteful, they produce inferior code." They were right about the code quality. They were wrong about what mattered.

Today, assembly programming still exists, but it's a niche. Not because it's obsolete, but because the abstraction layer moved up. Most problems don't need that level of control. The economics shifted: developer time became more expensive than CPU cycles.

AI represents a similar shift, but with a crucial difference: we're not moving up an abstraction layer. We're changing who—or what—operates at the current layer.

The Uncomfortable Truth About "Just Programming"

Let's be direct: the developer who only writes code—the "programmer" in the traditional sense—is facing an existential challenge.

This isn't about AI replacing developers. The data actually shows the opposite: AI amplifies developers, but it amplifies unevenly. Research from GitClear analyzing 30,000 datapoints shows that power AI users aren't new developers suddenly becoming productive—they're already top performers who have learned to orchestrate AI effectively.

Here's what's really happening: AI is exceptionally good at the routine, the pattern-matching, the "I've seen this problem before" cases. These used to be the bread and butter of mid-level development work. Now, an AI agent can generate a CRUD API, set up authentication scaffolding, or implement standard algorithms faster than you can sketch the requirements.

What AI can't do well—yet—is the architectural thinking, the creative problem-solving, the "this is a completely novel situation that requires genuine understanding" scenarios.

The uncomfortable implication: the role of "solid mid-level developer who executes well-defined tasks" is under pressure. Not immediately, not universally, but the trend line is clear.

Coding as a Hobby, Steering as a Profession

Think about mental math. We still teach children to add, subtract, multiply in their heads. Not because they'll never have access to calculators, but because understanding the underlying concepts matters. The skill becomes foundational knowledge, not daily practice.

Manual coding might follow a similar trajectory. In enterprise contexts where productivity and velocity are paramount, AI-assisted development will likely become standard practice. The economics are simply too compelling.

But here's where the assembly language analogy breaks down: we're not eliminating coding skill. We're transforming it from execution to orchestration.

The developer of 2026 and beyond will spend less time typing syntax and more time:

Architecting systems that AI agents can implement
Defining constraints and requirements with precision
Validating AI-generated code for correctness, security, and maintainability
Integrating components from multiple AI-generated sources
Debugging at a higher level when AI makes subtle conceptual errors

This isn't junior work that becomes senior work. It's a fundamentally different skillset: part architect, part quality assurance, part orchestrator.

The Silver Linings

If this sounds dystopian, consider the upsides—and they're substantial:

1. The Interesting Problems Remain

By delegating routine tasks to AI, developers are freed to focus on genuinely complex challenges. The problems that require deep thinking, creative solutions, novel approaches—these are what drew many of us to software development in the first place.

Senior engineers consistently report that AI tools let them spend more time on architecture and less on boilerplate. That's not a bug, it's a feature.

2. The End of False Trade-offs

How many times have you heard: "Yes, we should refactor this, but we don't have time"? Or: "Unit tests would be nice, but the business won't pay for it"?

AI agents don't get tired, don't have ego, don't argue about whether a refactoring is "worth it." They execute changes that would have sparked hour-long debates about ROI.

Technical debt has often been rationalized as a necessary evil—the price of moving fast. But what if the AI can implement the "right" solution as quickly as the hacky one? The excuses evaporate.

3. Experimentation Becomes Cheap

Want to try a controversial architectural change to see if it actually improves performance? In the past, this meant convincing the team, allocating time, potentially wasting days on a dead end.

With AI agents, you can experiment rapidly. Generate the alternative implementation, run benchmarks, gather data. If it works, great. If not, you've lost hours instead of weeks.

This changes the innovation dynamics fundamentally.

The Inconvenient Question We're Avoiding

But let's address the elephant in the room: How do we train the next generation?

If AI handles routine coding tasks, where do juniors cut their teeth? The traditional path—start with simple tasks, gradually tackle more complex problems—breaks down when AI can handle those "simple" tasks better than a junior developer.

This is a genuine challenge without easy answers. We'll likely need:

Deliberate practice environments where juniors code manually to build mental models
Apprenticeship models where juniors work alongside experienced developers orchestrating AI
Faster pathways from basic understanding to architectural thinking

This deserves its own deep dive (and we've explored aspects of it in previous articles). But the reality is: the industry hasn't figured this out yet.

Adapt or Fade

The most contentious claim: developers who resist this shift may find themselves increasingly marginalized.

This isn't a moral judgment. It's economic reality. If one developer can orchestrate AI to produce quality work 10x faster than manual coding, organizations will optimize for that capability. The market won't wait for everyone to be comfortable.

Some will argue that AI-generated code is inferior, that nothing beats hand-crafted solutions. They might even be right in specific cases. But they're making the same argument that assembly programmers made about C, that C programmers made about Python.

The question isn't whether AI code is theoretically optimal. It's whether it's good enough for most use cases while being dramatically faster and cheaper.

What This Really Means

The transition from coding to steering isn't about developers becoming managers or losing technical depth. It's about evolving from executors to architects.

Instead of writing code line by line, developers will:

Design systems at a higher level of abstraction
Define precise requirements and constraints
Validate and integrate AI-generated components
Make architectural decisions that AI can't
Handle the genuinely novel problems that resist pattern-matching

The developers who thrive will be those who:

Embrace AI as a collaborator, not a competitor
Develop strong architectural thinking, not just implementation skills
Master the art of precise specification—prompt engineering for code generation
Maintain rigorous quality standards even when code arrives faster
Stay curious about complex problems that still require human insight

The Time to Adapt Is Now

This isn't a prediction about a distant future. Engineers at Anthropic and OpenAI are already working this way. Other companies will follow within months, not years.

The good news: the most valuable developer skills—problem decomposition, architectural thinking, understanding business context—are precisely what AI can't replicate yet. But they need to be developed consciously, not assumed to emerge naturally from years of coding.

The transition might be uncomfortable. It will definitely be disruptive. But for developers willing to evolve from coding to steering, the opportunities are unprecedented.

The question is: are you ready to let go of the keyboard and pick up the conductor's baton?

Note: This article about AI-assisted development was written with AI assistance. Obviously.