In a world increasingly influenced by artificial intelligence, the journey from curiosity to mastery can often be traced through deliberate learning and personal transformation. In a recent episode of the Learn It All podcast, I had the opportunity to share insights from my professional journey, the 100 Days of AI initiative, and reflections on how AI is reshaping knowledge work and product management.
From Small Town India to Global Tech
My story begins in a small town in South India, where an early passion for math and computer science eventually took me to Epic Systems in Wisconsin—plunging me from tropical heat to Midwestern snow. At Epic, I worked on healthcare software, observing firsthand how technology integrates with one of the most human of professions—medicine.
Later, I transitioned to Microsoft, evolving from a software developer to a senior product manager. My current role is in Azure Files, focusing on unstructured data—an area central to today’s AI evolution. Along the way, I’ve also delved into angel investing and launched a podcast and educational content platform.
What Sparked “100 Days of AI”?
Like many, the emergence of ChatGPT was a moment of awe. But rather than just marvel at its capabilities, I decided to dive deeper. I drew on my background in machine learning and mathematics and committed to a self-directed curriculum: 100 Days of AI—an intensive learning and experimentation journey exploring large language models (LLMs), AI applications, and societal impact.
The idea wasn’t just to learn, but to document insights, build experiments, and share ideas openly. It was about turning passive consumption into active creation.
What Are Large Language Models, Really?
At their core, LLMs are prediction machines. When interacting with systems like ChatGPT, you’re essentially dealing with a model trained to predict the next most probable word or token in a sentence. Trained on massive corpora of internet text—compressed to around 45 terabytes—these models learn linguistic patterns at an astonishing scale. The result? Machines that can emulate understanding through sheer probabilistic power.
The Shift in Knowledge Work: A New Abstraction Layer
AI doesn’t eliminate work—it redefines it. Just as calculators and spreadsheets restructured mathematical labor, AI is altering the level at which we operate.
Consider blog writing. All five writers might use GPT-4 to draft a post, but the standout will be the one with better taste, structure, and storytelling instincts. Similarly, in law or accounting, AI can handle routine processing, while humans shift to higher-order verification and judgment.
For product managers, this shift is revolutionary. AI becomes “intelligence as a service”—an API-driven tool for clarity, ideation, and innovation. It’s less about jumping on the hype and more about integrating these capabilities to deliver true customer value.
Beyond Hype: Rethinking Product Form Factors
While apps like Notion and Clara are integrating AI effectively, I believe we’re just scratching the surface. Future breakthroughs will come from reimagining software form factors entirely. Think of Microsoft’s Recall, which lets users query their own digital memories, or Perplexity’s shift from “searching” to “answering.” The real excitement lies in first-principle thinking: creating AI-native experiences from the ground up.
Building a Learn-It-All Culture
Much of my growth at Microsoft comes from the “learn-it-all, not know-it-all” philosophy championed by Satya Nadella. It’s more than a mantra; it’s embedded in how teams operate, how performance is evaluated, and how innovation is nurtured.
For small businesses, building this culture starts with clarity of vision and investing in meaningful learning. Avoid the trap of shallow social content—opt for enduring sources like well-researched books and reputable platforms.
Becoming a Product Manager: Finding Person-Market Fit
Transitioning into product management was a process of self-discovery. I realized that while I could be a good engineer, my real strengths lay in intersecting technology with business and storytelling. Whether you’re a technical cloud PM or a growth-focused consumer PM, success comes from playing to your unique CPU—your cognitive strengths—not trying to emulate someone else’s.
Navigating the AI Landscape: Layers of Opportunity
The AI stack is multilayered:
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Foundation Models: OpenAI, Google, Anthropic, etc.
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Tooling & Orchestration: LangChain, LlamaIndex, etc.
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Applications: GitHub Copilot, Jasper, Perplexity
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Embedded AI in Existing Apps: Notion, Google Photos
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Cloud & Silicon Layers: Azure, AWS, Nvidia, AMD
The richest opportunities lie at the application layer. Yet even foundational models still hold room for disruption, with companies like Mistral pushing performance and cost efficiency frontiers.
Investing in AI (or Anything Else): Read Books First
If you want to invest in AI, real estate, or any domain—start by reading two or three solid books. Not YouTube shorts or influencer threads. Books like Venture Deals by Brad Feld or Angel by Jason Calacanis offer deeper, more actionable insights than any viral TikTok ever could.
Final Thoughts: Take the Hard Pill First
Whether you’re learning AI, becoming a product manager, or starting to invest, take the hard road first. Read, study, build, reflect. Quick wins are tempting, but long-term growth comes from deliberate practice and thoughtful learning.
“Before AI replaces you, a colleague who knows AI will replace you.”
Stay curious. Keep learning.
Nataraj is a Senior Product Manager at Microsoft Azure and the Author at Startup Project, featuring insights about building the next generation of enterprise technology products & businesses.
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