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.

Claire turned to face Zoe, her voice steady despite the emotion behind it. “I was nineteen when he started to have trouble remembering the more difficult equations. Within a few years, he couldn’t draw the face of a clock with the right time. Two years later, he didn’t even recognize my face. Something so fundamental … Continue reading →

If you’ve been hearing the buzz about AI but don’t know if it is for you, or you think your company is too small to use AI, I’ve got just the video for you. Since my recent posts predicting the election and some other price predictions, several people I talk with have been asking me … Continue reading →

I mentioned on linkedin that I thought it would be a fun diversion to build a PyTorch model to forecast the US Presidential Election. This was an interesting project for a few reasons. At the time I did this, it was about two weeks before the presidential election in the United States. As I write … Continue reading →

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 →

As many of you know, I have been building a personal AI assistant using Large Language Model transformers. The goal is to have it access documents and internet feeds to take care of different tasks. This week I have been experimenting with Meta’s newest iteration of their open source transformer, Llama3. I downloaded their 8B … 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 →