I'm Tiger Tyagarajan — CEO of Genpact (2011–2024), now a board member and senior advisor on AI, transformation and how organisations learn. My talks, frameworks, writing and enthusiasms, in one place.
My name is my operating manual. Tyāga — त्याग — is the Bhagavad Gita's word for renunciation, precisely defined: do the work with everything you have, and give up attachment to the fruits. Tyagarajan means “king of renunciation”. I can't claim the serenity — but I recognise the discipline, and its unlikely twin — curiosity.
Sixty years of being curious — an insatiable, slightly desperate need to keep learning, and therefore to keep asking questions — pulling me to the edge of change in industry after industry, and never letting me settle for what I already knew.
This site is where the two meet.
Giving back — knowledge, experience, scars, half-formed views — without attachment to how they land. And holding every view the Bayesian way, the way human beings have actually made progress for 3,000 years: state your prior, meet the evidence, update.
In a world where the unknown unknowns beat everything else we think we know, that isn't a philosophy. It's survival.
So read. Argue. Correct me. The continuous updating is the point. And that only happens by being curious, asking questions, debating, and changing.
Jabil
Inetum
HealthEdge
MathCo
BCG
Bain Capital
Brighton Park Capital
Innospark Ventures
Genpact
Bridge Green
ABSL
HFS
HTEC
Skan AI
ORO Labs
RevvUpThirteen years leading Genpact (NYSE: G), after serving as Chief Commercial Officer.
Built GE Capital International Services, the business that became Genpact; earlier CEO of its India consumer & auto finance JVs, later Global COO, Commercial Equipment Finance.
Each hub gathers the talk, writing, audio and one-pager for a single big idea.
I'm writing a book on curiosity — how it drives learning, leadership and a life well-examined. Writing starts around December 2026. This hub holds the research, tidbits, talks and drafts as they grow.
Inside: curiosity talks · research notes · the framework below · reading picks
What changes when AI does more of the work — and what leaders must do more of, not less: judgement, questions, culture and trust.
Inside: keynotes · framework pages · podcast conversations
My own podcast — conversations that challenge what you thought you knew, and update it. Every episode comes with its transcript and the takeaways worth keeping.
The frameworks I actually use, published in full — steal them, test them, argue with them.
A working sketch for the book — revised in public as the research develops.
I first wrote this down in February 2013, on my blog. A year later, in October 2014, I found myself giving the same answer on camera, in a short SarderTV interview — the same framework, twice over. The idea is simple: think of a career as three dimensions — the expertise you bring, the industry you work in, and the cultural context around you — and when you move, change only one dimension at a time. Here are the two versions, side by side: the written one, and the spoken one.
From “Tiger Bytes”, 25 February 2013.
Over the years I've come up with my own theory of how to build a career.
I think about building careers along three dimensions… let's call them X, Y and Z.
The X dimension is the type of skills that different roles require. So finance skills vs. technology skills vs. marketing skills etc.
The Y dimension is the type of cultural environment in which those skills are used and exploited. It could be which geographic regional culture like American vs. European or it could be the type of business… like family-oriented entrepreneurial group vs. multinational environment or it could be hierarchical vs consensus driven culture etc etc.
The Z dimension is the industry domain that that role belongs to…. Consumer financial services vs insurance vs healthcare vs retail etc.
Every role can be defined along these three dimensions and as you go through your career you must do three or four things. One you need to make sure you really become an expert at one dimension at least – and do that early in your career. I'm a big believer that as you go through a career, you'll always be known for one or two things that define you…. almost a safety net. So if everything else is lost, you can go back to your core.
The second rule is to change one dimension every time you move from one role to another. But don't change two dimensions if you can avoid it and please don't change three dimensions because that's a disaster!!
The third is that over a period of three or four moves, change all dimensions. So if you look at role number one to role number four, you should have changed all three. And then you're in a different culture, in a different industry, in a different domain!!
This way, you can almost chart your career as multiple potential options. So when I was working in highly change-oriented organizations like Unilever, Citibank, and then GE, every time I was asked to move jobs within the organization, it took me pretty much ten minutes to say yes or no because all I needed to do was fit it into this model. So I changed six roles in three years with Citibank. I changed four roles in six years with Pond's and Unilever and I changed five roles in five years with GE and GE Capital in the early part of my career at GE…. I said yes in 100% of those situations but that's a different story I will tackle another day!!
But like most successful 'mantras', this one has to be a blend of art and science. The dimensions and rules are the science part of building a career… but you need equal parts of 'art' to build a successful career and this comes from investing in longevity, relationships and passion.
The generation that is getting into the workforce today is more impatient, more exposed, and they probably are more knowledgeable. They want everything in life… their gratification has to be instant!! When we hire people and look at resumes, we don't care what a great job someone is doing today or yesterday, if the resume shows six jobs in ten years, they have zero chance of even being called for an interview. Zero chance! We just don't call them because no amount of telling me why they did what they did is going to convince me that they're going to last.
Equity gets created only when time is spent. Knowledge gets created only with time… when you combine intellect with experience. So one of the biggest things that I would tell people coming out of universities into the workforce and joining the workforce today is do not underestimate time, and do not underestimate relationships and both require equity. If someone were to ask me the one joy that I've got over twenty-five years of a career, I would say it's seeing people who have worked with me, who have worked for me, and are now in probably bigger jobs than even they dreamt of. And I can look at them and say part of the reason they are who they are is because of the fact that, “we worked together.”
And the last thing I'd say is to be extremely passionate about what you do. Like Steve Jobs said do what you're passionate about and be passionate about what you do, otherwise don't do it. When the two match, there is no difference between work and play. I work twenty-four hours though I might not be in office… because I love what I do. I'm really passionate about what I do. And I think if people actually gravitate to doing things, whatever it is, that combine those two, then I think the chances of being hugely successful is so much better.
A year after writing the blog, I was asked the same question on camera — what career advice do you give young people? — and gave the same answer in two and a half minutes.
Watch the interview · 2 min 26 secSarderTV interview, October 2014 · 2 min 26 sec.
It is the blog, spoken: don't fixate on the industry or the function; choose the learning environment; think in three dimensions; change one at a time; love what you do. Hearing it again is what prompted the update below.
Thirteen years later, I reread this with some nervousness. Most of it still holds.
The three dimensions are still the map. Become genuinely expert at one thing, early — that expertise is your safety net for life. When you move, change one dimension at a time, so you are learning instead of drowning. Choose the learning environment over the fashionable function — the “what” of a job will change many times; whether you are being challenged will not stop mattering. And passion is not decoration. It is the fuel.
What has changed is the ground under the framework. In 2013, the dimensions moved when you moved. Today, the first dimension — the expertise itself — is being rewritten by AI every two or three years, whether you move or not. The finance, coding, marketing or analysis skills you mastered at twenty-five will not sit still until you are forty. So the rule matters more, not less: if one axis is being changed for you, you had better be deliberate about the other two.
And look at the job the AI industry has invented. The FDE: hold the expertise axis deep, then rotate the industry and the culture — a hospital this quarter, a factory the next, a bank after that — and learn inside the client's real environment, not in a classroom. The most sought-after role in AI today is, more or less, this blog post turned into a job description.
Now the confession. In 2013 I wrote that if a resume showed six jobs in ten years, the candidate had zero chance of being called for an interview. Zero. I meant it, and I said equity and knowledge are created only with time in one place.
I have changed my mind.
Today, when I look at a resume, I actively look for the opposite signal as well: people who have worked across multiple companies and multiple environments, who have taken risks and moved. Companies do not live as long as they used to. Neither do roles. Depth still matters — but depth is no longer the same thing as staying. You can now build real depth across places, not only inside one place, and the person who has collected contexts often sees what the one-company lifer cannot.
Which is perhaps the most important update of all. I said the things above in 2013 and 2014 with complete conviction. Most of them, I still hold. One of them, I have reversed. That is not inconsistency. Holding a belief so hard that no evidence can move it is the real career risk — in fact, it is the one move this framework never allowed for: changing nothing at all.
Update your priors. Including the ones you published.
First published on the Tiger Bytes blog, 25 February 2013 · Interview: SarderTV, October 2014.
Keynotes, interviews and conversations — where each happened, when, and what it was about.
Opening keynote · RECODE Amsterdam · 30 Sep 2026 · 26 min
Why AI transformations fail to deliver ROI — and the blueprint for the ones that don't: process reinvention, the 70% people challenge, the AI value ladder and the learning flywheel.
Watch the keynote · 26 minPrefer the slides? View the keynote deck (PDF) — also listed under Presentations.
29 Sep 2026 · Recording session
A recorded conversation with ORO Labs on AI, procurement and how large organisations actually change.
24 Sep 2026 · Madrid · Live dinner session
Speaking at the NextGen CEO Forum dinner in Madrid — on leadership transitions and staying a learner after the top job.
Decks from recent talks — each with a one-paragraph takeaway.
Slides from the opening keynote at RECODE Amsterdam, 30 Sep 2026 — the talk you can watch under Talks. Takeaway: AI adoption is no longer the differentiator, execution is — reinventing process and operating models, not running more pilots, is what turns adoption into return.
40 slides
In October 2026, AI cracked problems mathematics had chased for decades. Forty slides on what broke, what it means for the young starting out, and why every field is next.
The visual, colourful deck behind my curiosity talks. Takeaway: the five-step curiosity practice — notice, ask, follow, connect, update.
Follow a theme across posts, talks and podcasts.
Something happened in October 2026 that I cannot stop thinking about.
In a single month, a frontier AI model produced serious mathematical results across seventeen fields of mathematics — 719 manuscripts, hundreds of result families. Problems the community had circled for decades, some for a century, suddenly had machine-generated proofs attached to them: a blowup construction for the Navier–Stokes equations, the quasi-Riemann hypothesis pushed past barriers that had held for generations, a resolution of the Unique Games Conjecture.
I am not a mathematician. I am something arguably more dangerous: a lifelong amateur, a man whose whole career has run on a slightly desperate need to keep learning. So I did what I do. I went and listened to the mathematicians themselves — in this case three of the best, Steven Brunton, Ankur Moitra and Alex Kontorovich, trying to make sense of their own earthquake in real time.
Here is what I have concluded. The earthquake is not that machines can now do mathematics.
The earthquake is what it reveals about the difference between an answer and understanding.
For as long as any of us have been alive, those two things arrived together. To get the answer, someone had to understand. The struggle was the tuition; the proof was the receipt. October separated them. A proof can now be formally verified — checked line by line by unforgiving software — while no human being alive can honestly say they understand why it is true, or what it means, or where it leads.
Answers have become cheap. Understanding has not. That gap is the whole story.
Consider the strangest detail of the month. One of the conjectures the machines settled belonged to a living mathematician, Subhash Khot. He declined to read the proof. Not out of pride — out of something closer to grief. Once you have seen a solution, you cannot unsee it. The years you might have spent inside the problem, building the intuition that only that struggle builds, are gone. A spoiler, at the scale of a life's work.
Now multiply Khot by a generation. Somewhere right now there is a brilliant 24-year-old who chose her problem the way researchers always have — carefully, bravely, as a bet of five years of her life — and woke up to find a model had posted a solution overnight. The old playbook said: pick a hard problem, be patient, become the person the problem makes you. The playbook is not wrong, exactly. It is unfinished. Nobody yet knows what the new one says, and the people who most need it are the youngest, with the least power to rewrite it.
That is the part I keep returning to. Not the machines. The young.
Because here is the thing nobody in business should miss: mathematics is not the exception. Mathematics is the preview.
Every field gets its own October. The order is not random. Fields fall in the order of how cheaply their answers can be checked. A proof can be verified by software in seconds — so mathematics went first, and went completely. Where verification is slower, messier, more human — medicine, law, engineering, management — the same wave arrives later and lands differently. But it arrives. The pattern is always the same: producing answers gets cheap; telling a good answer from a bad one, and knowing which question was worth asking, gets expensive.
I have spent forty years watching technologies do this to industries. The scarce skill is never the one being automated. It is the one standing next to it.
So what stands next to answer-making? Judgement. Taste. The ability to look at fifty machine-generated results and sense which one matters. The stubbornness to ask why a verified proof is true, not merely that it is. And underneath all of it, the engine I have bet my own life on: curiosity — not as decoration, but as discipline. The questions were always the human part. Now they are almost the only part.
There is a metaphor from that conversation I cannot shake. For centuries, mathematicians climbed the mountain on foot, and the climbing was the education — you arrived at the summit as someone the ascent had made. Now there is a helicopter. It will happily drop you at the top. The view is identical. The understanding is not. The future belongs to people who, knowing the helicopter exists, still understand what the climb was for — and choose, deliberately, where to climb on foot and where to fly.
Mathematics has survived its own death before; it has reinvented itself after every crisis for three thousand years. It will do so again. The question October asks the rest of us — in boardrooms, in classrooms, in every profession that trades in answers — is whether we will be as honest as the mathematicians were forced to be, about what our work actually consists of once the answers are free.
I have gone deep on this — the breakthroughs, the spoiler problem, what I would now say to that 24-year-old, and why your field is next — in a 40-slide presentation I have put together from that conversation. If this essay has done its job, it has made you curious. The deck is where the curiosity gets fed.
Go deep: The Mathematics Singularity — 40 slides (PDF)
Read it. Argue with it. Correct me. That was always the point.
The seed idea for the book: curiosity isn't a personality trait, it's a practice — notice, ask, follow, connect, update.
From boardrooms and advisory work: when machines get better at answers, human value moves to judgement and question quality.
The idea behind my podcast of the same name — holding views strongly enough to act, loosely enough to revise.
My X, Y, Z framework for building a career — written in 2013, said on camera in 2014, and updated for the age of AI and the FDE.
Books and podcasts, grouped by genre.
My library, shelf by shelf — the same six genres, in the same order, as the podcasts below.
The shows I follow on Spotify, grouped by shelf — tap a title to open it.
Dwarkesh Podcast
Lex Fridman Podcast
COMPLEXITY — Santa Fe Institute
Closer To Truth
Making Sense with Sam Harris
The Michael Shermer Show
C-Squared Podcast — chess, with CaruanaThis shelf is waiting for its first shows — the language podcasts I follow will live here.
Mostly vegetarian, a seafood lover, dinner early at 19:00. Red wine, Port and sake. Lisbon since April 2025 is home — this corner holds restaurants, markets and bottles worth travelling for.
Constant travel for boards and speaking — with deliberate stops for family in New York and Boston, and for culture wherever the calendar lands.
Amateur astronomer and stargazer. Cricket — India, always. Football — Portugal, France, Spain and Brazil. Chess — ~1100 on chess.com, working to improve the honest way: by studying, not by hoping.
Short, regular and manageable: one idea, one talk or post worth your time, and one thing I'm reading or listening to. Named Bites — the site is Bytes.
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Via my executive assistant, Sandra Kaszer — tyagarajanassistant@gmail.com · LinkedIn · X
