Two knowledge workers starting at $75k in 2015 now face dramatically different futures: by 2030, the AI-skilled one reaches nearly $150k while the non-AI user stagnates around $80,000, backed by PwC’s 2025 data showing a 56% wage premium for AI proficiency and wages growing twice as fast in AI-exposed roles. AI acts as a powerful skill multiplier, its exponential leaps compress years of progress into months, and together they’re driving a stark K-shaped economy where early adopters soar while others see their labor commoditized in a shift from human effort to capital-owned intelligence. The divide is real and widening—your future income may hinge on which side of the K you choose to stand on.

Retrieval-Augmented Generation (RAG) is a powerful way for enhancing AI tools to leverage proprietary data and generate more accurate, relevant responses.

The more I work in Langchain, the more I discover its strength for creating powerful, custom Large Language Model interactive AI powered chatbots. For example, right now I am using it to create a custom chatbot that is trained on our proprietary internal documentation, reports, and log files. This uses something called RAG (Retrieval Augmented … Continue reading →

Over the next serveral weeks I will be diving into my work in creating a personal assistant, much like a private chatGPT. The main reason will be so I can explore the different capabilities of LangChain and how I can use it to do exploratory data analysis on my library of PDF files, emails, and … Continue reading →

All work and no play, right? Yet, I’ve found that setting time aside to play can be rewarding, especially when it comes to doing something that lets me learn and gain a deeper appreciation and understanding of my coding. Ever thought about how empowering it feels to be a creator? Even though I’ve been working … Continue reading →

When I chose my final senior thesis and project for my computer science degree I chose to write an artiificial intelligence based model that used a custom machine learning code that I trained to predict future housing prices based on macroeconomic data. I wanted to dive more into machine learning and what sorts of insights … Continue reading →