A few days ago, I was thinking about something that has been bothering me.
If AI can write the proposal, build the presentation, analyse the market, create the marketing plan, draft the content and even help build the product…
what exactly happens to experience?
I have spent a large part of my life accumulating it.
Eighteen years in the corporate world. Yahoo. Vodafone. Airtel. Sony Ericsson. Different businesses, different bosses, different markets, different problems.
Then entrepreneurship.
And entrepreneurship taught me a very different set of lessons.
Some of them rather expensively.
I once spent close to ₹10 lakh building a website.
Eighteen months later, it had sold practically nothing.
No AI tool could have saved me from that lesson afterwards.
Because the lesson wasn’t:
“Don’t spend ₹10 lakh on a website.”
That would be too simplistic.
The lesson was about clarity.
I had built before I had understood deeply enough what somebody would actually pay me for.
That scar has stayed with me.
And today, when I look at somebody’s business idea, I often notice that question before I notice the website, the logo, the funnel or the technology.
That is what experience does.
It changes what you notice.
There was a time when knowledge itself was an advantage
When I started my career, information was harder to get.
The person who knew more often had an advantage.
You learnt from your manager.
You learnt from books.
You learnt by sitting in meetings where people more experienced than you made decisions.
You learnt by getting some of those decisions wrong yourself.
Twenty or twenty-five years of that created a fairly meaningful gap between someone who had lived through situations and someone who had only read about them.
AI has changed that equation very quickly.
Today, somebody can ask:
“Give me a go-to-market strategy.”
And get one.
“Create a 90-day content plan.”
Done.
“Write a sales proposal.”
Done.
“Help me build a business model.”
Done.
And much of it will be reasonably good.
That should make experienced professionals uncomfortable.
It certainly made me think.
But I have started coming to a different conclusion.
AI is making knowledge cheaper. It is not making experience worthless.
It may actually be making experience more valuable.
Just in a different way.
The difference is easier to see when something goes wrong
A strategy looks wonderful when it is sitting inside a presentation.
The real test begins on Monday morning.
The customer does not behave the way the research predicted.
The employee you were counting on resigns.
The supplier misses the deadline.
The campaign gets clicks but no sales.
The founder becomes the bottleneck.
The partnership that looked obvious on paper starts becoming complicated.
And suddenly the answer is no longer sitting neatly inside a framework.
You have to judge.
I saw this repeatedly during my corporate years.
The technically correct answer and the answer that would actually work inside that organisation were not always the same thing.
Sometimes the numbers told you one story and the people in the room told you another.
Sometimes a decision made perfect sense financially and was still the wrong decision.
You only start recognising these things after seeing enough situations.
That is difficult to put into a course.
And it is even harder to put into a prompt.
I realised this more clearly after leaving corporate life
When you leave a large organisation, something strange happens.
For years, your experience lived inside a machine.
There was a brand behind you.
A team around you.
Processes.
Systems.
Technology.
Customers.
Distribution.
Budgets.
Access.
Then one day, much of that disappears.
The laptop goes back.
The access card goes back.
And somebody asks you a deceptively simple question:
“So what exactly are you doing now?”
That question can be remarkably difficult to answer.
Because you are not starting from zero.
But you are starting without the machine that made your experience useful.
I went through my own version of this.
After years inside large companies, I became an entrepreneur.
I built things.
Some worked.
Some didn’t.
COVID forced another reset.
There were moments when I had plenty of experience and still didn’t have clarity about what to build next.
That was humbling.
It also taught me something I wish I had understood earlier.
Your experience does not automatically become an advantage.
You have to convert it into one.
That is where I think AI becomes very interesting
There are two ways I can use AI today.
The first is to ask:
“What should I do?”
And let AI give me an answer.
Sometimes that is useful.
But there is a second way that I find far more powerful.
I can tell AI:
Here is what I have learnt.
Here is how my customer behaves.
Here are the patterns I keep seeing.
Here is what failed last time.
Here is what I believe.
Here is what I am uncertain about.
Now help me think.
Help me research.
Help me structure it.
Help me build it.
Help me test it.
Help me communicate it.
That feels like an entirely different relationship.
I am not asking AI to replace my experience.
I am using AI to put leverage behind it.
That is why the phrase I keep returning to is:
Clarity Inside. Leverage Outside.
The machine can provide extraordinary leverage.
But the clearer the thinking you bring into it, the more useful that leverage becomes.
There is another question hiding underneath all this
If you have twenty, twenty-five or thirty years of experience, perhaps the question is not:
“What new thing should I learn so that I remain relevant?”
Of course we should keep learning.
I am learning constantly.
But there may be another question worth asking first.
What do I understand today that I could not possibly have understood twenty years ago?
Think about it.
Which customer can you understand unusually well?
Which problem have you watched repeat itself dozens of times?
What mistake can you spot before somebody else sees it?
What conversation have you had so many times that you already know where it is heading?
What looks obvious to you today only because it once took you five painful years to understand?
That is not merely experience.
That is accumulated context.
And context is difficult to manufacture overnight.
This matters enormously for a Second Innings
I meet experienced people who believe they need to begin again.
Another qualification.
Another certification.
Another course.
Another year of preparation.
Sometimes that is exactly what is required.
But sometimes I wonder whether we are looking in the wrong direction.
Maybe your next opportunity is not hiding inside the next thing you learn.
Maybe it is hiding inside something you already know so well that you have stopped recognising its value.
The trick is to make it useful to somebody else.
Your twenty-five years cannot simply remain twenty-five years on a résumé.
They have to become something.
A point of view.
A method.
A service.
A product.
A framework.
A business.
A community.
A way of solving a very particular problem for a very particular person.
That is when experience begins turning into leverage.
And I would go narrower, not wider
This is another lesson I have had to learn myself.
When we start something new, the temptation is to make it useful for as many people as possible.
I understand why.
A larger audience feels safer.
But AI is getting very good at serving the general audience.
If somebody wants generic marketing advice, generic career advice, generic business advice or generic AI advice, there will be an unlimited supply of it.
So I am increasingly interested in the opposite question:
Who can I understand unusually deeply?
Not one million people.
Perhaps the first hundred.
What stage of life are they in?
What are they frightened of?
What have they already tried?
What do they secretly worry they may have left too late?
What does their spouse worry about?
What does financial security mean to them now compared with twenty years ago?
Why do intelligent, experienced people still remain stuck even when they know a lot?
Those questions fascinate me.
Because those are not technology questions.
They are human questions.
Perhaps this is the real opportunity AI has created for experienced people
AI is reducing the value of generic output.
Good.
Let it.
Let AI draft the first version.
Let it research.
Let it summarise.
Let it automate the repetitive work.
Let it build the spreadsheet.
Let it help write the code.
I don’t need to compete with a machine on those things.
I would rather spend my time on the things my first innings taught me to see.
People.
Patterns.
Trade-offs.
Judgement.
Consequences.
And increasingly, context.
Because when everybody has access to similar technology, the question changes.
It is no longer:
Who has the better tool?
It becomes:
Who knows what to do with the tool - and for whom?
That is a very different game.
And I think experienced people may be far better positioned for it than we realise.
So if you are somewhere around the beginning of your Second Innings, I will leave you with one question:
What have the last twenty or thirty years taught you that somebody else would gladly pay to understand in twenty minutes?
Don’t dismiss the answer because it feels obvious to you.
It may only feel obvious because you paid for it with twenty years of your life.
- Vinay
Clarity Inside. Leverage Outside.