There’s a strange moment in every career where you know the person you need exists — the expert, the founder, the advisor — but you simply don’t know how to find them.

Not because they’re obscure.
But because the tools we’ve had weren’t built for the way we think.

LinkedIn’s new AI-powered people search feels like it’s finally addressing that gap.
Not with another filter.
Not with keywords.
But with something that looks and feels like a real question.

This isn’t a headline that screams disruption, but it’s a quiet shift that will change how professionals move through opportunity.

The News

(Facts taken exclusively from TechCrunch’s Nov 13, 2025 article by Ivan Mehta.)

TechCrunch reports that LinkedIn is rolling out an AI-powered people search feature for premium U.S. users, with plans for expansion.

Users can now search with natural-language queries such as:

• “Find investors in the healthcare sector with FDA experience.”

• “Co-founders in NYC who built a productivity startup.”

• “Who in my network can help me understand wireless networks?”

Before this, LinkedIn relied heavily on lexical search and manual filters.
Rohan Rajiv, Senior Director of Product Management, told TechCrunch that older search methods often hid relevant people because users didn’t know the right combination of titles or keywords.

Additional points from TechCrunch:

• Early users used this tool for job shifts, business growth and career guidance.

• LinkedIn is widely used in demos for AI agents and browser-based assistants.

• The company hasn’t yet restricted how AI agents access profile data.

• Premium users will see “I’m looking for…” in their search bar.

• The feature is imperfect — different variants of a query (e.g., “YC” vs “Y Combinator”) return different sets of people.

• LinkedIn is actively improving query interpretation.

These are the facts.
The meaning sits beneath them.

Why This Matters Now

Search has always been a quiet force on LinkedIn.
Most people don’t talk about it.
But it shapes a huge portion of the platform:

• Who finds you

• Who you find

• Which opportunities surface

• How teams identify talent

• How creators discover collaborators

When search is rigid, networks are rigid.
When search becomes conversational, networks become fluid.

This change matters because navigating professional relationships has historically been a skill of its own — one learned through trial, error, and an awkward number of cold DMs.

AI lowers that friction.

You don’t need to know which titles map to which responsibilities.
You don’t need to remember which industries call similar roles by different names.
You don’t need to wrestle with filters that approximate what you mean.

You just describe the person you’re looking for.

What Is Being Built or Changed

Unlike other AI launches that focus on speed or automation, LinkedIn is trying to shape understanding.

1. Discovery becomes intent-driven, not keyword-driven

A user’s question can reference:

• Experience (“FDA experience”)

• Outcomes (“built a productivity startup”)

• Geography (“based in NYC”)

• Context (“who can help me understand wireless networks”)

This is how humans naturally search — based on purpose, not syntax.

2. Network exploration becomes meaning-aware

Until now, networks felt like a static graph.
You either knew someone existed or you didn’t.

AI changes this by surfacing:

• Specialists who don’t label themselves perfectly

• Founders who pivoted but remain relevant

• Operators with niche but impactful experience

• Second-degree connections who fit your exact need

The gap between “someone who could help you” and “someone you know about” shrinks.

3. Personal profiles become signals for machine interpretation

Your work history is no longer scanned only by humans.
It’s parsed by the search model.

Clear narratives matter.
Context matters.
Specificity matters.

Your past experience becomes a searchable dataset.

This quietly reshapes what a LinkedIn profile is.
It becomes a structured, machine-readable representation of your journey — and the accuracy of that representation affects whether you appear in someone’s query.

The BitByBharat View

I’ve seen this kind of shift before — not on LinkedIn, but inside early teams.

When you’re scaling a company, finding “the right person” often takes more time than building the feature you need them for.

You ask friends.
You scroll through lists.
You try keywords.
You try again.
It’s messy.

And in many cases, you don’t find the right person because you didn’t know how to describe them in a way the system understood.

That’s why this update hits deeper than it looks.
LinkedIn is moving from:

“searching for keywords” → “searching for capability.”
“searching for titles” → “searching for trajectories.”
“searching for labels” → “searching for lived experience.”

The implications of that shift are huge.

Professionals aren’t defined by their titles anymore — they’re defined by the patterns in their work.

And LinkedIn is finally giving users a tool that respects that.

The Dual Edge (Correction vs Opportunity)

Correction

If your profile is vague or generic, AI search will expose that.

People who describe their work clearly will be surfaced clearly.
People who don’t will be skipped — not intentionally, but mechanically.

This isn’t a prompt-tuning game.
It’s a clarity-of-story game.

Opportunity

For founders and creators, this change does something subtle but powerful:

It levels the field.

If you’ve done meaningful work — even in emerging markets, niche sectors or undervalued roles — you can now be found based on what you’ve actually done, not on whether you guessed the right keyword in your headline.

For developers and builders, there are also new product opportunities:

• Tools that help people optimize for AI discovery

• Services that analyze what queries your profile appears in

• Niche “AI-search overlays” for founders, fund managers, or creators

• Better deal-flow or expert-discovery engines for specific geographies

• Conversational search layers built on top of LinkedIn workflows

This is a new layer in the professional stack.

Implications (Founders, Engineers, Creators)

For Founders

Treat your LinkedIn profile as a technical surface area.
Make your story explicit:

• Industries

• Roles

• Stages

• Outcomes

• Transitions

• Contexts where you operate best

AI cannot surface what you do not articulate.

For Engineers

Look beneath the launch and see the primitives:

• Semantic retrieval

• Profile embeddings

• Ranked relevance

• Context expansion

• Disambiguation logic

These elements will shape the next wave of professional tools.

For Creators and Operators

Don’t wait for search to find you — help it find you.

Clarity is leverage.
Specificity is leverage.
Good narrative structure is leverage.

Your network is no longer built only by meeting people.
It’s built by being discoverable.

Closing Reflection

Networking used to be about effort — showing up, reaching out, digging through lists, hoping for a match.

Now a single query might surface someone you should have met five years ago.

AI is not replacing connection.
It’s removing the friction around it.

If you’re building anything today — a startup, a career, a creative path — it’s worth asking:

What opportunities appear when discovery becomes intention-driven?

Because the people you find through that question may change everything that comes next.