100× Your Software Development Skills: How AI is the Next Big Leap

When I started programming decades ago — writing VB6 components, transitioning into .NET, learning C#, building automation scripts — I witnessed first-hand how each new toolset unlocked previously impossible productivity gains. Today, we’re in a similar moment of transformation thanks to AI—tools like GitHub Copilot, ChatGPT and other AI Agents / LLMs that are enabling developers to produce far more business value with their time than ever before.

From VB6 → .NET → AI: The evolution of developer productivity

Back in the VB6 era, writing components for Windows was the standard way to build reusable logic. Then came .NET, with managed code, better frameworks, and tooling that made it easier to ship reliable apps faster. Each shift wasn’t just incremental — it re-defined what we could build and how quickly.

Now, AI and LLM-driven tooling are the next wave. These tools aren’t simply “another editor extension” — they are turning into copilots for you, the developer. They help with code completion, code generation, automation scripts, even architectural scaffolding.

The parallels are clear:

• VB6 and .NET provided abstractions and frameworks so developers could stop reinventing low-level plumbing.
• AI tools are giving developers higher-level “thinking” and “generation” assistance so we can stop reinventing routine logic and focus on the why and what.
• The result: we can spend less time on repetitive tasks and more time delivering business-impacting solutions.

How I’m using Copilot to scale my work

Here’s a more concrete look at how I’ve been using GitHub Copilot (and related AI tools) in my daily workflow — and how they’re helping me deliver at a level that feels like I’m a “100x developer” now!

• Boilerplate & scaffolding: Instead of writing the same class skeletons, interfaces, logging plumbing, DI setup over and over, I prompt GitHub Copilot to generate the scaffolding. That removes minutes (sometimes hours) of repetitive typing and focus.
• Automation scripts: Working across Microsoft Azure, .NET, Python, HashiCorp Terraform Infrastructure-as-Code and more, I’ve been able to lean on AI to help write code in any language I need (PowerShell, Bash, Python, C#, etc) to write scripts that automate aspects of my work. This allows me to iterate on the development process much faster.
• Debugging & exploration: When I hit unfamiliar problems to solve, new APIs, or need to integrate a new service, I’ll ask GitHub Copilot (or another AI-tool) for best practices, usage patterns, example code, how to call a API, parse the results, or how to best modify existing code with new functionality. AI really accelerates learning, iteration, and execution.
• Quality & review: While I still do the primary architecture, design decisions, review the code, and manually write code myself, the AI helps me iterate more quickly, get to a “good draft” state faster, which means I can spend my time on the high-leverage aspects (e.g., UI/UX, business logic, security, edge-cases). I’m always the human in the loop, but AI is like an entire team of developers and engineers ready to assist me at any time.
• Focus on value, not typing: The biggest shift is mindset: I’m spending less time tied typing small details and more time tied to deliverables. Because the machine helps with the small details, I can invest in what matters — solving a business problem, improving performance, architecting for scale.
• Discovering new possibilities: Because I can iterate on tasks faster, it takes far less time to talk to Copilot and have it help you figure out an innovative solution to a problem. What may have taken weeks or months to prototype, GitHub Copilot can do in hours or days.

Why this matters: It’s not about gimmicks – it’s about leverage

There is a very important distinction here: AI isn’t magic. It doesn’t replace the developer — it amplifies the developer. Just like when VB6 or .NET didn’t eliminate software developers, but instead let them build more complex things more reliably, so too does AI shift the kind of work we must do.

Here’s why I believe this moment matters:

• Efficiency: You can get working code written faster. That has real business value — faster time to market, lower cost, fewer burn hours, better ROI.
• Productivity: Instead of treating every feature as a “build from scratch” exercise, you can reuse more, scaffold more, iterate faster. That means more features, more automation, more value.
• Focus on high-leverage work: Tasks that require judgment, architecture, domain knowledge, trade-off thinking remain human. AI frees you from the boring details so you can focus on value; although you still need to understand the details.
• Competitive advantage: Developers and teams that embrace this shift towards AI will deliver more with the same (or even less) effort and budget. Just like early adopters of VB6 and .NET had an edge, developers who master AI-enabled workflows will take the lead going forward.
• Evolution, not revolution: This is just the next step in a sequence of innovations. It’s helpful to recognize that AI is just another tool, and you will either embrace AI or be replaced by a person who embraces AI.

Some caveats and my advice

Of course, with these new AI-powered skills comes new responsibilities and pitfalls. From my decades of experience, here’s what I recommend:

• Don’t treat AI-generated code as “done by magic”. Review it, test it, understand it. You are the human in the loop that can’t be replaced.
• Use the tool’s output as drafts, starting points, accelerators, not as the final product. Always review and iterate.
• Keep your architecture, domain knowledge, edge-case handling, performance and security thinking firmly in your hands. Use your expertise to know what to prompt the AI.
• Make sure your automation scripts and scaffolding are maintained, versioned, documented. Just because something was “generated by AI” doesn’t mean it doesn’t need to adhere to your coding standards.
• You are the human in the loop that’s reviewing code, writing code, solving problems, and collaborating with the AI. AI changes how we build, but not why.
• Be patient. The AI ecosystem is constantly evolving. Tools will improve. Practices will evolve. So will your own AI-enabled skills and workflow over time. You will only become more efficient with practice and experience using the AI tools over time.

Conclusion

If you think of your career as someone who delivers business value, instead of merely a programmer, then you have the mindset shift necessary to take your career to the next level using these AI tools. Just like when we moved from writing raw C++ memory management to leveraging .NET frameworks (or many other previous innovations), we’re now moving into a world where AI and LLMs help us move from writing every line of code to orchestrating solutions, automating pipelines and tasks, and delivering value at a new scale that was previously impossible.

By embracing tools like GitHub Copilot, you don’t just “write code faster” — you elevate your entire development process. You free yourself to focus on the architecture, the domain, the business logic, the differentiation, the business value. You become less about “how many lines can I type” and more about “how much value can I deliver”. That shift is real. That shift is meaningful. And you’re in the middle of it.

At the end of the day, always remember, software is written for people and without people there would be no software. As a result, our job as software developers is it to deliver business value to the people that depend on it.