AI agents are changing how we write software, and here’s the twist: the teams best positioned to win aren’t the ones who code the fastest, it’s the ones who document the best.

I’ve been watching and exploring the agentic AI wave closely, and I keep seeing the same pattern. Teams struggle with AI coding assistants because the AI “misses context” (about 65% of developers report this). Meanwhile, the teams with solid documentation and clear standards are seeing their productivity soar.

Here’s the irony: for years we’ve claimed “the code is the truth” and treated documentation as an afterthought. Now those same documentation practices we’ve been ignoring are becoming our most critical skill.

The shift from programming to engineering is here

Anthropic’s CEO warns that nearly half of entry-level white-collar positions could be eliminated. Salesforce and Meta aren’t hiring junior developers anymore. So yes, programming jobs might be going away.

But here’s what matters: people are resilient and programmers who adapt will thrive and see results they’ve never seen before. The shift isn’t about AI replacing us, it’s about programming becoming less about writing code and more about defining what needs to be built and vitally; controlling the result.

Think about it: when an AI agent can generate thousands of lines of code in minutes, the bottleneck isn’t implementation anymore. It’s specification! A vague requirement like “build me a login system” produces vague, potentially insecure code. But prime the AI with best practices and give it a detailed spec with authentication flows, session management requirements, and security constraints? Now you’re cooking!

Forget “vibecoding” where you just vibe with the AI and hope for the best. That’s a recipe for disaster. This is about becoming an architect who orchestrates AI to build better systems faster. Programmers who learn these skills will find even greater power, compensation and job safety.

Documentation: From Afterthought to Superpower

For years, we’ve treated documentation like that vegetable on your plate you know you should eat but never do. “The code is the truth,” we’d say, as if that excused us from explaining our architectural decisions or business logic, or at best we kept it in a digital whiteboard or shared folder.

That needs to change, and fast.

GitHub just released a spec-driven development toolkit where “intent is the source of truth” and specifications become executable. Google’s dev tools manager uses AI to write 100-line requirement docs before any coding begins. The industry is shifting, and those without good documentation are getting left behind.

Here’s what comprehensive documentation enables:

  • AI agents that understand your system architecture and design decisions
  • Consistent code generation that follows your patterns
  • Faster onboarding of both humans AND AI agents
  • Clear boundaries and integration points

The brutal truth is that programmers need to start writing specs and documentation NOW. Not next quarter, not when you “have time.” A recent METR study found that experienced developers using AI tools actually work 19% slower on their own repositories. Because the AI lacks context that good documentation would provide.

Quality Matters More Than Ever

There’s a valid concern that AI will enable a flood of crappy applications. And you know what? It probably will. But that’s exactly why professional developers become MORE valuable, not less.

In a world where anyone can prompt an AI to generate code, the differentiator becomes:

  • Systems that actually scale under load
  • Applications that don’t leak user data
  • Codebases that can evolve without falling apart
  • Software that actually works reliably

Quality, security, and maintainability will command premium prices. The market will quickly separate professional AI-assisted development from amateur “prompt and pray” applications.

The Timeline Is Tighter Than You Think

Nearly 80% of IT leaders are planning to invest at least $1 million in AI agents this year. By 2026, we’ll hit mainstream adoption. The window for building these new skills is now.

Right now (Q4 2025): Early adopters are establishing their advantage 2026: Best practices crystallize, mainstream adoption begins
2027 and beyond: AI-assisted development becomes table stakes

If you’re not learning to write specs, improve documentation, and review like an architect RIGHT NOW, you’ll be playing catch-up while others are already optimizing their second-generation AI workflows.

The New Programming Skills

So, you want to stay relevant? Here are the skills that matter now:

Write Crystal-Clear Specifications

  • Study design patterns and best practices and algorithms
  • Define acceptance criteria that leave no room for interpretation
  • Identify edge cases before they become bugs
  • Articulate security and performance requirements
  • Map out integration points and dependencies

Review Like an Architect

  • Spot when AI introduces anti-patterns or technical debt
  • Ensure security governance in code and tools (AI loves to leak secrets if you’re not careful)
  • Maintain architectural coherence across AI-generated components
  • Validate that the code actually solves the business problem

Establish and Enforce Standards

  • Create AI-friendly coding guidelines
  • Build automated checks for AI-generated code
  • Maintain audit trails of what was generated when and why

Google’s Ryan Salva puts it perfectly: “Your job as a developer is going to look a lot more like an architect.” We need to learn these skills now, not when it’s too late.

Where To Start

Here’s some recommended reading to get started with agentic coding in the right way:

How to start:

  1. Start documenting your system architecture and key decisions, and do it in your repo where AI can read it
  2. Practice writing detailed specifications for your next feature (take help/inspiration from the links above)
  3. Try AI code review tools on a small project to learn their quirks

Next:

  1. Level up your specification writing—this IS the skill that matters
  2. Shift code reviews from syntax-checking to architecture and security focus
  3. Create governance policies for AI-generated code
  4. Design new systems to be “AI-friendly” (clear boundaries, good abstractions)
  5. Build your unique advantages into documented processes AI can follow
  6. Keep learning, this space moves fast!

The Bottom Line

We’re entering an era where programmers become architects and conductors rather than typists. The skills that matter are shifting from implementation to specification, from coding to reviewing, from building to governing.

The brutal irony? The very things we’ve traditionally avoided—documentation, specifications, establishing standards—are becoming our survival skills. That “the code is the truth” mentality? It’s about to make you unemployable.

Teams that recognize this shift and adapt will thrive. Those clinging to “I’m valuable because I can code” will struggle. And those who abandon all engineering discipline for “vibecoding”? They’ll produce the garbage that makes the rest of us look good by comparison.

The future belongs to developers who can specify precisely, document thoroughly, and review intelligently. The question isn’t whether to develop these skills—it’s whether you’ll learn them fast enough to stay relevant.

I’m excited about this future. This is a tremendous shift from programming to engineering - making engineers even more valuable if you adopt these skills now.

The train is leaving but it’s not too late. Are you on it?