If Dinosaurs Knew AI was Coming


If dinosaurs had known that a meteorite was going to be the trigger of their demise — they would have done something about it. Jose Marino, 2026

Recently, while applying for a Senior .NET position, I was asked two questions that forced me to think carefully about architecture, AI, and the way I work as an engineer. Rather than answering with generic interview talking points, I used the opportunity to describe WORKS Commons—the AI-native platform I have been building—and my philosophy for working with AI.

I reproduce those answers here.

Describe a system you’ve built or owned in production using your core stack—what key architectural or design decisions did you make, and why?

I currently own and develop an AI-native platform called WORKS Commons (WC), built with .NET 10, Blazor, EF Core, and OpenAI integrations for signal processing.

One of the primary architectural decisions was adopting the MVVM pattern. I wanted a clear separation between the UI and presentation/business logic so that the front end could evolve independently or even be replaced by a different client application in the future. The View is responsible only for rendering and user interaction, while the ViewModel handles validation, state management, and presentation logic.

The system is organized using a layered architecture. Business rules are implemented within Manager classes, while data-access services follow a Transaction Script approach for CRUD operations and are responsible for maintaining a clean Unit of Work. I also use DbContextFactory to ensure explicit DbContext lifecycles and reliable transaction boundaries.

Because the platform processes AI-driven workflows, I designed what I refer to as an Agentic Workflow Architecture (AWA). In this model, LLMs transform structured inputs into structured signals, which are then converted into workflow metadata that drives application state. Importantly, the model does not make business decisions; workflow intent and control remain within deterministic application logic. This approach allows AI capabilities to be incorporated while maintaining predictability, auditability, and governance.

For scalability and throughput, I implemented producer-consumer patterns to support concurrent processing of workflow tasks. Combined with the separation of concerns provided by the overall architecture, this has resulted in a system that is testable, maintainable, and extensible as new workflow capabilities are introduced.

The next question was about how I have approach Artificial Intelligence. It gave me the opportunity to deliver a One-Two jab.

How has AI changed the way you work as a developer, and how do you use it in your day-to-day workflow? 

AI has significantly changed how I develop software, both as a tool within my workflow and as a capability I incorporate into products.

In my day-to-day development work, I primarily use GitHub Copilot and other LLM-based tools as reviewers, collaborators, and research assistants rather than as generators of complete solutions. My preference is to first design and implement a solution myself, then use AI to review the code, identify edge cases, suggest improvements, or help unblock specific problems. This approach keeps ownership and decision-making with the developer while providing the model with sufficient context to generate higher-quality recommendations.

I also use AI to accelerate learning when working in unfamiliar domains. In those situations, I use carefully crafted prompts to explore concepts, generate initial implementations, or draft unit tests. The generated output serves as a starting point that I validate, refine, and test. I find this process valuable because it both increases productivity and helps me learn new technologies and patterns more quickly.

Beyond coding, I use AI extensively for requirements, issue tracking, and technical documentation. As the sole developer of WORKS Commons, I rely on GitHub to manage requirements, workflows, and defects. AI helps me refine requirements and issue descriptions so that they are clear, precise, and semantically consistent. Well-written requirements improve communication and provide stronger context for both human collaborators and AI-assisted development workflows.

Building WORKS Commons has also influenced my perspective on AI. The platform itself uses LLMs to solve specific business problems, which has reinforced my belief that AI is most effective when it augments human expertise and operates within well-defined workflows rather than replacing engineering judgment.

Conclusion

What struck me after writing these answers was that they describe the same idea from two different perspectives.

In WORKS Commons, the model contributes intelligence, but the workflow retains control. The AI does not own intent; the system does.

In my daily work as a developer, the same principle applies. AI contributes ideas, reviews, documentation, and suggestions, but responsibility for the design remains with the engineer.

Many discussions about AI focus on replacement; however I believe that the greatest value comes from amplification. AI allows me to move faster, learn faster, and communicate more clearly, but it does not remove the need for engineering judgment. If anything, it increases the importance of that judgment.

The dinosaurs never saw the meteorite coming. We do.

The question is not whether AI will change software development. It already has.

The question is whether we will learn how to use it effectively while keeping agency, responsibility, and intent where they belong.

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