I've been finding myself thinking a lot about young Ryan lately. That kid who would spend hours hunched over the family Mac, figuring out how to program his first game in HyperCard. I can still feel the rush of successfully animating that crude stick figure to move across the screen - I was convinced I'd just created the next Duke Nukem. Every new discovery on that Power Mac 9500 felt like unlocking a superpower. I thought the possibilities were endless.
That same childlike excitement has returned, but this time it feels different. The endless possibilities I thought I had on that Mac were not so limitless, as it turns out. But today, with the breakthroughs in deep learning and neural networks powering this recent wave of AI tooling, it actually feels like the possibilities are truly endless. We’re not just getting new tools — we’re witnessing a fundamental shift in how humans interact with computers. That’s why I’m embarking on a 100-day journey to deeply understand AI from the ground up.
I’ve spent countless hours with ChatGPT, Claude, and Perplexity, working them into my routines to multiply my output as a product manager. I use generative assistants like v0 and Cursor to design and ship at lightning speeds. These aren’t just incremental improvements, they’re transformative. Things that used to take me weeks to do, like research, prototyping, or doc writing, only take minutes now. When I hit a bottleneck, my first thought is how AI could help clear it. It’s shocking how quickly thats become a natural reaction — AI has become an intuitive extension of my workflow.
Why now?
My edge as a product manager has always come from diving deep into emerging technologies. For the past decade, I've built expertise by immersing myself completely – whether in music tech, mobile growth, or platform architecture. This approach turns identifying and navigating customer problems into instinct.
The timing couldn't be better. 7 out of 10 businesses have already adopted AI and majority are planning to increase their efforts over the next 2 years (McKinsey & Co, 2024; Accenture, 2024). For product leaders, this isn't just another tech trend - it's a fundamental shift in how teams will operate. We're going to see entire product development cycles compress from months to weeks, and companies that adapt their processes will outpace those who don't by orders of magnitude. Workforces will transform, industries will reshape, and trillions in value will be created. The friction that exists in today's AI tools will vanish in months. I'm not just interested in keeping pace as an early adopter — I want to help bridge the gap to mainstream adoption.
The 100-day plan
I've always built to learn, so that's exactly what I'm going to do. This journey breaks down into three core components:
Learning: I'm going back to data science fundamentals, learning from leaders like Andrej Karpathy and Andrew Ng. I'll be diving into their practical deep learning courses, focusing on techniques for fine-tuning LLMs and building robust neural networks. Karpathy’s “Zero to Hero” series and Ng’s Deep Learning courses will improve how I think about model design and the business of AI. I'll talk with experts who launch and scale AI products, while getting hands-on with the technology. The goal isn't just to understand how to best use data, but to develop the intuition for where and how this can create the most value.
Building: Theory meets practice. I'm building my personal AI agent to automate the repetitive parts of my PM workflow - from summarizing messages to prioritizing tasks. I'll ship a web app with a public API (built entirely with AI assistance) that helps teams implement AI within their existing workflows. Along the way, I'll train a few basic models myself, each project pushing my understanding while creating something immediately useful for other product teams.
Contributing: Communities like r/MachineLearning and Hugging Face have already taught me so much. I plan to give back by sharing what I learn, while offering the customer empathy and strategic thinking muscles I’ve developed over my 12 years in tech.
Besides the obvious: Reshaping how we work
While mastering AI tools personally is crucial, the bigger opportunity lies in reimagining how entire organizations operate. The real opportunity is not using AI to do business-as-usual faster. It's about reshaping how we work to maximize AI's impact.
Today’s internet was built for human consumers and creators. Now we’re seeing the emergence of a new layer — one built for both people and autonomous agents. This means entirely new interfaces need to be dreamed up, and companies like Vercel, Postman, and /dev/agents are leading that charge. The challenge of second-order thinking falls back on today's builders and companies. Those who figure out how to rewire their processes and build a culture of constant experimentation will unlock AI's true potential. This is the challenge that keeps me up at night – and gets me excited to jump out of bed each morning.
From AI user to builder
These tools are incredibly powerful, yet still malleable in terms of how they'll impact humanity. I've spent my career obsessing over how software can make lives better. This journey isn't just about personal growth – it's about understanding intimately how AI can help my life, my teammates' lives, and hopefully, at scale, many more people's lives.
I'm sharing this commitment publicly not just for accountability, but to hopefully inspire others to dive deep with me. In 100 days, we'll see where this leads. Sometimes the smallest stones create the biggest ripples. Whether it's helping another PM implement their first AI workflow, or building tools that make AI more accessible to non-technical teams, each step forward compounds. The insights and tools from this journey won't just impact my work - they could help shape how the next generation of products gets built.
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Want to follow along or join the journey? I'll be documenting my progress and learnings - sharing practical insights about AI implementation, tools that work, and honest reflections about what doesn't.
Feature image was generated via the Flux Pro image generator.