About me

Origin

I was not born in Silicon Valley. I did not go to Stanford. I have lived in multiple continents, multiple cities, played multiple roles. By the time I arrived in the technology industry, I had already learned that a place is not the same as a perspective. {NEEDS HUMAN INPUT: a specific origin moment Deepak wants to share -- a city, a memory, a first computer he touched. Bridge call captures.}

What I did have, early, was a kind of stubborn curiosity about how things actually worked. Not just software -- silicon. The chips themselves. I was lucky to be young while computing was still something you could see, hold, plug into a board. The mystery of the machine was visible. You did not have to take anyone's word for what was happening -- you could open the case.

Career arc

I rode several waves. Silicon, when chips were the news. Analog computing, in its quiet long dusk. The .com era, when the web was inventing itself in real time and we were inventing ourselves alongside it. The internet maturing into something ordinary. Each one taught me something different about how technology actually arrives in people's lives versus how it gets hyped.

What I did not have was the Stanford-Google insider lens. I worked across geographies. I worked in roles where the customer was a person, not a metric. I learned what San Francisco professionals sometimes never learn: that what people actually need from technology is often quieter than what engineers want to build for them. {NEEDS HUMAN INPUT: 2-3 specific role markers Deepak wants to anchor on -- which industries, which jobs, what era. Bridge call captures.}

That breadth is the asset. Not the depth in any single thing. The shape of the arc.

The pivot

A few years ago I started building startups. The motivation was simple: I had pain points. Real ones. Things I had faced as a person living in the world. And I wanted to fix them.

What I ran into is what every product owner runs into. The gap. Between what you want users to feel and what your engineering team ends up shipping. The translation loss is enormous. Months go by. The thing that ships is recognizable but not what you imagined. You start to wonder if you imagined it wrong, or if the structure of the work has flattened it.

Then AI tools arrived. Specifically the ones where you can describe the product you want and watch it appear, more or less, on your screen. I went into beast mode. I was building full-fledged multi-tenant apps, one a week. The gap collapsed.

And then I noticed something else. The foundation models were releasing features quarterly that ate the wrappers. The thing I built in week three would be obsolete by week sixteen, not because I stopped working on it but because the big AI labs shipped a feature that did the same thing for free. I was a wave behind, every quarter.

I could have become an AI consulting agency. Helped people implement these features. There is a market for that. But I kept thinking about a different audience. The people I was meeting. The people who watched silicon, analog, .com, and internet arrive, and then watched AI arrive like a supersonic jet that flew by so fast they could not even see where it landed.

That image stayed with me. The supersonic jet. People who had once been the experts -- people who built the infrastructure the rest of us live on -- standing on the runway, watching something blow past them and wondering if they had time to catch up.

They have time. They have, in many ways, the best context. They have lived through more technology transitions than any other generation. Their pattern recognition is exactly what they need. But the path is less obvious now, and most of the AI commentary online is written for people who are already in the wave, not for people standing on the runway.

That is the gap I want to close.

What I do now

I have run two cohorts of retired Silicon Valley professionals through a curriculum I built. About fifty people. They learned the fundamentals of AI -- not the math, the concepts. They learned which wrapper tools are worth using in daily life. They learned how to generate content with AI that sounds like them, not like ChatGPT. They learned how to publish, how to grow a small audience, how to position themselves as thought leaders in a field they already know.

Some of them started writing books. Some of them started building things. A few are well on their way to becoming solo builders -- people who use AI to build their own products, alone, without an engineering team. {NEEDS HUMAN INPUT: confirm the "two cohorts" and "$250K revenue" can appear publicly, or anonymize numbers/specifics. Bridge call captures.}

That is the work. Helping people who watched everything come back to the wave. Connecting the dots from silicon to AI so the new wave makes sense in continuity with everything they have already learned.

What is next

I write here. I teach in cohorts. I am still in apprentice mode myself with AI -- I am farther along than my readers, but I am not the master. The framing is “learning together” not “teaching from above.”

If any of this resonates, the best ways forward are to read the articles, sign up for the occasional notes, or write back. I read everything.