Knowledge is getting cheaper. Judgment isn't.
For twenty years I have asked one question: what is this actually worth? I ask it of products as a pricing strategist, of people's work as AI rewrites it, and of history, where most of our answers were tested first.
One question, three lenses.
What a product is worth, and why most companies leave that answer to habit. Twenty years of pricing at Dell and Cohesity.
Pricing writingWhat a person's work is worth once knowledge is free. The Knowledge Quadrant Framework, and the tools I build around it.
The frameworkHow people decided what mattered long before us. From Akbar's revenue reform to Lucknow and Mauryan India.
History & India
"The question nobody is asking clearly enough is not whether AI will take your job. It is which part of what you do is actually yours."
The framework maps how people create value at work, and how exposed that value is to AI. Two axes. Knowledge: explicit knowledge is documented and searchable, the kind AI replicates at scale; tacit knowledge is judgment earned by living through real situations. Output: accuracy means there is a right answer and being wrong costs something; originality means there is no right answer, only a fresh one.
Surgeons, lawyers, analysts, engineers writing production code. High stakes, verifiable output. AI is entering this room fastest.
Exposure: highestMarketers, designers, copywriters, product communicators. Pattern-based creativity at scale. AI is displacing the volume tier first.
Exposure: highStrategists, senior leaders, negotiators, advisors. Consequential calls on incomplete information. The human premium zone, and it is growing.
Exposure: lowerWork that comes from lived judgment and a point of view no one else holds. This quadrant creates history. AI can assist but cannot originate.
Exposure: lowestInside the four quadrants live eight personas, from The Doer (highest displacement risk) to The Oracle (minimal). Most people are not where they think they are. I built Kairos to close that gap.
Nights-and-weekends products, each one testing an idea I care about. Some are polished. All are real.
A self-inquiry tool built on the Knowledge Quadrant Framework. Describe your actual work in your own words, and get back your quadrant blend, your dominant persona, and one question to sit with.
Why I built it: the AI-and-jobs debate happens at the level of industries. The real answer lives inside your own day.
A tap-only quiz show: 500 questions in English and Hindi on India's economy, society, wins, entertainment, and tech, with a real source behind every answer. Ten questions, about two minutes.
Why I built it: love of country should come with the real numbers attached.
An ecosystem for older people, built around the phone they already carry: a companion to talk to, carefully curated and composed content, and a setup that either a caretaker or the elder can manage on their own.
Why I keep coming back to it: ageing parents are the users technology most often forgets, and they deserve something designed with patience.
A family AI that runs on my own computer. Each family member is linked to it, and it keeps a persistent memory, so it carries our context from one conversation to the next. Dyuti means light.
Why I built it: the most useful AI is one that knows you, and I only trust that when the memory lives at home.
I use AI to research, code, and backtest investment and trading strategies, then judge each one on the evidence rather than the story. It is pricing discipline applied to my own decisions.
Personal research only, not financial advice. I am not a licensed or certified financial advisor.
A trainer that takes you from zero to table-ready: ten short lessons, an adaptive coach that deals you more of the hands you get wrong, timed tests with no hints, and a strategy chart heat-mapped with your own mistakes.
Why I built it: blackjack is the cleanest classroom for judgment I know. The math is settled; the discipline to follow it is the hard part.
A daily startup-guessing game for operators, investors, and anyone who likes reading a business from its outline.
Why I built it: pattern recognition is a tacit skill. A daily game is the cheapest way I know to practise it.
I give Claude, Gemini, Kimi, GLM, and local open models the identical one-shot brief and compare what each one actually ships: the code, the design choices, and the bugs.
Why I run them: an executive's opinion on AI should come from using it, not reading about it.
What a 1574 disaster and a 1580 tax reform taught me about building systems that outlast the people who build them. Akbar, Raja Todar Mal, and the oldest revenue leak in the book.
I speak to leadership teams, pricing and commercial teams, and MBA classrooms. Every talk pairs a clear framework with stories people remember, often from history.
The Knowledge Quadrant Framework, live. Where AI is actually displacing work, which skills are gaining value, and how to move your team toward them.
Why most pricing is inherited rather than designed, and what dynamic, value-based pricing looks like in B2B SaaS and aftermarket businesses.
In 1574 Akbar's land survey collapsed. By 1580 Raja Todar Mal had turned its data into a revenue system that outlived the empire. Fix the unit, average the noise, publish the price book: lessons for every pricing and finance team.
A hands-on session on how a non-engineer executive goes from idea to working product with AI, drawn from the tools in my Lab.
30 to 45 minutes, with Q&A. In person or virtual.
Half day. Participants map their own work on the quadrant using Kairos.
For MBA and executive-education classrooms, with a case discussion.
I take a limited number of invitations each quarter. Tell me about your audience and what you want them to leave with.
Invite me to speakI did not set out to become a pricing strategist. I set out to understand how things work, and what they are worth.
Since 2022 I have led pricing strategy at Cohesity through its move to cloud-native SaaS. The Knowledge Quadrant Framework came directly from that work: watching knowledge get commoditized in real time and needing precise language for what was happening.
Before that, Dell was my fifteen-year graduate school. Across India and Round Rock, Texas, I built prediction models, recommendation systems, and pricing architectures under some of the largest commercial decisions the company made. Earlier, Coca-Cola taught me what no engineering curriculum does: the gap between what something costs and what someone pays is a human problem, not a math one.
I started at Infosys at 22, building trade-processing systems for Goldman Sachs, and watched sophisticated work get systematized the moment someone found the repeatable pattern. The offshore model was the original displacement engine. AI is the second one, and I intend to understand it from the inside.
Outside work, I read and write history. I am writing The Arithmetic of Mercy, a novel set in Mauryan India, and I keep returning to Lucknow and Awadh, where so many of my own stories begin.
New writing, Lab notes, and Part 3 of the Knowledge Quadrant series before it goes anywhere else. No noise.
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I have spent twenty years inside complex commercial problems: pricing built for a different era, deals that look healthy at the top line and bleed at the margin, renewal pricing that was inherited rather than designed, and AI capability still waiting for a business model.
I am not taking on paid projects right now. If you are working on something interesting in B2B SaaS pricing, aftermarket pricing, or AI monetization, I am open to a conversation on a pro-bono basis.