I turned 42 this year. I don’t usually attach much meaning to birthdays, but this one did trigger a quiet pause—not a reinvention, not a reset, just a sense of recognition. A feeling that certain instincts and interests I’ve carried for a long time were finally meeting the right conditions to be acted upon.
In many ways, it felt like coming full circle. Post–IIT Bombay, I had toyed with the idea of building something of my own more than once, but the timing never really worked. The cost of experimentation was high, the downside felt asymmetric, and meaningful execution required a kind of commitment—in time, capital, and headspace—that didn’t quite align with where life was then. AI didn’t create those ambitions. It removed enough friction that acting on them no longer felt irresponsible.
There’s a popular mental model floating for AI—the Star Trek computer. Voice interface, simple commands, complex tasks executed seamlessly. It’s appealing: tell the AI what you want, and it handles the messy details. But we don’t have that yet. What we have now is quite powerful but far messier—and that gap between expectation and reality matters.
Looking back, this arc probably started earlier than I realized. What felt like casual tinkering at the time—experimenting with tools, workflows, and ways of interacting with information—was really the beginning of a longer loop of building, reflecting, and writing.
⸻
The first half of the year was deliberately focused inward. The arrival of our newborn changed the rhythm of everyday life in fairly fundamental ways. Time became scarcer, but priorities became clearer. Decisions that once felt nuanced or debatable started resolving themselves quickly when viewed through the lens of family well-being and long-term sustainability.
Around the same time, we moved from Dubai back to Mumbai. On paper, it was a relocation. In practice, it was a broader reset—of cost structures, support systems, and optionality. Some things became simpler, others more complex, but overall it created a sense of grounding that had been missing.
In hindsight, the first half wasn’t a slowdown so much as an incubation period. That stability mattered more than I realized at the time, because once the personal base felt settled, professional decisions became easier to make—and easier to commit to. The questions that kept surfacing during this phase were telling. Education, work, and what we’re really preparing the next generation for stopped feeling abstract and started feeling personal. Agency matters more than intelligence—the capacity to take initiative, make things happen, shape your path rather than wait for it. Are we educating for that? It’s a question that feels more urgent when you’re thinking about your own child’s future.
⸻
The second half of the year marked a clear shift from exploration to ownership. I chose to go down the solo path, not because it’s easier, but because at this stage it offers speed and coherence. Fewer dependencies, tighter feedback loops, clearer accountability.
AI changed the feasibility equation in a very real way. What once required teams and capital can now be prototyped solo—not perfectly, but fast enough to learn. Over time, this also changed how I approached building itself, gravitating toward a more fluid, iterative style where intent and execution sit much closer together.
That conviction led to formalizing OrchestratorAI. Registering the company and filing the trademark weren’t about signaling externally as much as they were about drawing a line for myself. This wasn’t just advisory work or experimentation anymore; it was something I wanted to build patiently and seriously.
A lot of the focus naturally gravitated toward the long tail—especially MSMEs. Large enterprises will adopt AI, but slowly and expensively. Smaller businesses can leapfrog. The marginal cost of intelligence has dropped enough that problems once considered too small or not worth solving suddenly are. That idea kept resurfacing as I looked at broader patterns in work, strategy, and go-to-market, often in ways that felt far messier in practice than in slides.
Completing a year self-employed felt like its own milestone—not because of what I’d built yet, but because I’d committed to the path.
⸻
Three things that have crystallized
This is a great time for builders—not for shortcut-seekers.
There’s a popular narrative that AI is about doing less work—the Star Trek computer fantasy where you state your intent and complex systems just work. My experience has been the opposite. We don’t have the Star Trek computer. AI rewards those willing to go deeper, not those trying to bypass fundamentals.
Tools amplify intent and effort; they don’t replace them. The gap between “prompting” and actually building systems—workflows, artifacts, and feedback loops—is widening.
Jevons’ Paradox is no longer theoretical in knowledge work.
Making intelligence cheaper doesn’t reduce the amount of work; it expands it. Lower costs unlock suppressed demand—more ideas get tested, more workflows get built, more edge cases start to matter.
Entire categories of previously unsolvable problems suddenly become economically viable. We’re even seeing fundamental business model shifts—from selling seats to selling outcomes, from “buy software” to “hire agents.”
This is the foundation of what I’m building: serving markets that were previously uneconomical to serve.
A lot of old ideas are finally working the way they were meant to.
State machines, artifact-centric design, structured workflows, even the promise of auto-coding—none of these are new concepts. What’s new is that the economics finally make sense.
But there’s also a new layer to master. Programmers now need mental models for agents, subagents, prompts, contexts, tools, workflows—a fundamentally new abstraction layer intermingled with traditional engineering.
Abstractions still leak, and much of the year’s noise around agentic coding oscillated between hype and reality before settling. What’s emerging: structure matters, and there’s a real shift as agents become central to how work gets done.
⸻
One curious footnote
Starting in September, I noticed an unusual spike in traffic to my blog—specifically to posts from 10-15 years ago. The pattern was unmistakable: China. Most likely LLM training runs scraping old content at scale.
There’s something quietly amusing about that timing. While my decade-old posts were feeding tomorrow’s AI models, I was using today’s AI to finally act on ideas I’d shelved post-IITB. Full circle, in an unexpected way.
⸻
2026 feels different. Not because the work gets easier, but because the constraints are clearer. Family grounded, venture formalized, year one complete.
