Are Really "Generalists becoming more valuable than specialists.” ?

You might remember the two product managers from my last blog . Both were solving the same problem — high drop-off at the checkout page.

Same AI tool.Same context. But completely different outcomes.

Both came from strong academic backgrounds. Both knew their craft.

And yet…One solution was clearly more thought-through.

The second product manager explored multiple problem areas before arriving at a solution.The first product manager went straight to a solution. I’ve started seeing this pattern more often.

Give the same problem to a team of three people. Same brief. Same tool. Same context. You’ll still get very different approaches. And almost always, one of them stands out.

Clearer, Sharper ,More practical.

So the real question is:

Why does one approach work better than the others?

At first, I thought it was experience. Or domain knowledge.

But that’s not it. The difference lies in something much more fundamental:

How they think.

More specifically—

Whether they think deterministically… or probabilistically.

Let’s go back to the same example. Two product managers. Same problem — high drop-off at checkout, especially at the KYC step.

PM1 (Deterministic Thinking)

Prompt:

“How can I reduce the drop-off rate at the checkout page, specifically at the KYC section? Are there design issues that can be improved?”

Notice what’s happening here. The question already assumes the problem is design-related. So the AI follows that direction. It generates suggestions around UI, UX tweaks, layout improvements. PM1 gets answers. But within a narrow frame.

PM2 (Probabilistic Thinking)

Prompt:

“Can you list all possible reasons why users drop off at the checkout page, especially at the KYC step? I’m trying to brainstorm and identify root causes.”

Now the approach changes. Instead of jumping to a solution, PM2 expands the problem space.

The AI explores multiple dimensions:

• UX friction

• trust issues

• KYC complexity

• time taken

• document availability

• user intent mismatch

  
  

PM2 is not solving yet. She is understanding first. And that’s the key difference.

PM1 approached the problem with a pre-formed belief:

“This is likely a design issue.”

So the AI followed that path.

PM2 approached the problem with curiosity:

“What are all the possible reasons?”

So the AI expanded her thinking.

It’s possible PM1 relied on past experience. And PM2 may not have seen this exact problem before. But interestingly, that worked in her favour.

Because instead of forcing a solution… She allowed the problem to open up.

And that’s what made her analysis more well-rounded.

This is the difference between deterministic thinking and probabilistic thinking.

And this is where the real advantage with AI begins.

This Is Also Why “Generalists” Are Suddenly Trending

This shift also explains something you’ve probably been hearing a lot lately:

“Generalists are becoming more valuable than specialists.”

At first, it sounds like LinkedIn hype. But there’s something real underneath it.

In a deterministic world, depth was everything. Knowing the answer was the whole game.Specialists thrived because expertise meant certainty — and certainty was and is still valuable.

But in a probabilistic world, the nature of problems changes.

There often isn’t a single right answer, but asking the right question and asking the question rightly is what is key. There are multiple directions. Multiple possibilities.

And the value shifts — from knowing to deciding.

And deciding well requires a different kind of capability.

• Connecting ideas across domains

• Evaluating trade-offs

• Understanding context

• Seeing the bigger picture

  
  

This is why you’re seeing this shift.Not because generalists suddenly became smarter.But because the nature of work has changed.

It’s not that specialists become irrelevant.

And if you go back to the two product managers… That’s exactly what you saw. One narrowed the problem too quickly. The other expanded it before deciding.

That difference? That’s not experience.That’s not tools.

That’s probablistic thinking - AI mindset

So How Do You Build This Capability?

If probabilistic thinking is the difference…

the obvious next question is:

How do you actually build it?

From what I’ve seen, it comes down to three very practical shifts.

1. Exploring Possibilities

Most people use AI to get an answer. Probabilistic thinkers use it to map options.

Instead of typing: "What's the best GTM strategy?" — they ask for three fundamentally different ones. Aggressive growth. Capital-efficient. Partnership-led.

Now they're not reacting to one idea — they're choosing between directions.

That's a completely different kind of thinking. And it leads to completely different results.

2. Evaluating Trade-offs

Here's where real judgment kicks in.

AI will give you options all day. What it won't do is decide for you. And most people still want it to — they're still looking for "which one is best?"

But that's the wrong question. Every option carries trade-offs. Faster growth vs. higher burn. Speed vs. quality. Scale vs. control. The real question isn't which option is objectively better. It's: which trade-off makes sense for my context, right now?

There's no perfect decision. There are only conscious ones.

3. Thinking in Systems

This one's the most underrated — and honestly, the hardest to develop.

Most people evaluate ideas in isolation. But nothing in real life works in isolation.

Change your pricing, and you've also changed customer perception, sales incentives, retention conversations, and positioning. Pull one thread and the whole sweater moves.

If you don't see the system, you'll misjudge the outcome.

Bringing It All Together

So go back to those three people with the same problem and the same AI tool.

The first one takes the first answer and runs with it. Fast, but shallow.

The second explores a bit more. Gets a few options. Picks the one that feels right.

The third does all three. Explores widely. Evaluates the trade-offs honestly. Thinks about the system it all sits inside.

That third person consistently produces better results. Not because they're smarter. Not because they're better at prompting. But because they're better at thinking.

The Real Shift

AI doesn't make you better just by giving you answers. It only makes you better if you bring the right thinking to the conversation.

The managers who struggle with AI aren't less capable. They're just expecting certainty from a system built for probability.

And the ones getting ahead?

They've stopped asking: "What's the right answer?"

They've started asking: "What are the possible ways to think about this?"

That's the shift. Simple to say, harder to actually do. But honestly — that's the skill that's going to matter most in the years ahead.

That’s a lot to take in. In the next post, I’ll break down how to actually practice and build this capability of probablistic thinking. Till then stay foolish and keep expeirmenting .

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This is part of my ongoing work helping managers build an AI mindset—practical ways to think, decide, and work in the age of AI. It's not about mastering tools, but understanding how to work intelligently with intelligence. Looking forward to your feedbacks