The story
I've been starting things since high school, forty-three of them. Most died fast, a few limped, every one taught me something. Then I picked up a book about thinking machines, and the obsession that followed outlasted every business before it.
2016
After my bachelor's I started where the safe paths go: finance and consulting. It took one look at the data side to know which part I couldn't put down.
So I switched into people analytics and spent five years learning the basics the hard way. Tableau, Power BI, and the craft of making numbers change real decisions.
2017
In 2017 I picked up Thinking Machines: The Quest for Artificial Intelligence and Where It Is Taking Us by Luke Dormehl. It walks the whole story of AI, from Alan Turing and the Cold War labs, through the expert systems that stalled out, to the neural networks quietly waking up inside our phones. The line that grabbed me: AI was not coming someday. It was already here, and most people had not noticed.
I tore through fourteen more AI books that year like something was chasing me. But one idea from that first book would not let me go: affective computing, teaching machines to read human emotion. Not to sell more ads. To help people who cannot easily read faces get through a world built on them.
2019
Then I found the person already living that idea. Rana el Kaliouby had co-founded a startup called Affectiva out of the MIT Media Lab. Her team used machine learning and computer vision to recognize emotion in images of human faces, and the use that hit me hardest was helping people with autism understand what the faces around them are feeling. The exact thread I had underlined in a book two years earlier was out there, running as a real company.
So a couple of months into my master's, I flew out to meet her in person and asked for her guidance: how could I make a real impact in AI? That conversation set me on my path to become one of the best data scientists I could be. November 18, 2019. I kept the photo.
2020
I did the Master of Management in Artificial Intelligence at Smith School of Business, a one-year intensive, as one of the youngest in the class.
It was the COVID year. No ceremony, no gown, no photo of the moment. So I made the photo that should exist: same degree, same campus, same pride, rendered from my own likeness.
2020-2022
By day I was a data scientist, then lead data scientist, at RVezy. Think Airbnb, but for RVs and motorhomes. I built the models that told the business what a customer was really worth.
At night I taught. I was a professor at the University of Ottawa for Introduction to Data Science and Analytics for Decision Making, and I ran bootcamps at Lighthouse Labs that walked a hundred-plus career changers into data jobs.
2022
I was a business intelligence engineer at Amazon when ChatGPT dropped. The day after, I posted a Fiverr gig that said prompt engineering. It was one of the first five generative-AI gigs on the entire platform.
Within months the gig ranked number one in the world for the keyword prompt engineer, pulling 50,000 to 100,000 impressions a month. Business number forty-three had found its moment.
2023-2025
As data science manager at ODAIA, Greek for tricks of the trade, I brought machine learning and generative AI into the drug industry, where the rules are strict and the stakes are real.
Meanwhile the gig had a name now: Prompt Advisers. More than two hundred AI projects and counting, built the same way I learned everything else. Ship it, teach it, repeat.
2025
The YouTube channel passed 80,000 subscribers. Early AI-dopters grew to 1,300 practitioners with twelve coaches. I keynoted our own sold-out event in Berlin. The photos on this site are from those two days.
2026
Prompt Advisers crossed seven figures and Entrepreneur wrote the profile. More than two hundred AI projects delivered, five O'Reilly live courses taught, and a book on the way: Thinking in AI.
It started with a book called Thinking Machines. It comes full circle with one called Thinking in AI. Forty-three businesses to learn one lesson: obsession beats chasing trends.
The rest of the story publishes as it happens, on the channel and inside the community.