AI-Native Talent: The Gluten of Tech Recruiting

Just like gluten, everyone's talking about it. Almost nobody can actually explain it.

AI-Native Talent: Here's What It Actually Means

What does AI-native talent actually mean? You are starting to see this bubble up more and more in job descriptions, but very little clarity on what this actually means. 

You see the internet scattered with answers to this question. 

"Someone who gets AI"

"They've worked with AI tools before."

"It's like an AI-first mindset."

We've created a hiring requirement that sounds critical, feels urgent, and means absolutely nothing in most cases. 

So let me offer a definition.

My Definition of AI-Native Talent

AI-native talent is someone who instinctively seeks leverage, experiments independently, and has demonstrated the ability to use AI as a force multiplier in their domain.

Not someone with "AI" in their title.

Not someone who can explain transformers.

Not someone who took a prompt engineering bootcamp.

Someone who has actually shipped something that made them, or their team, meaningfully more productive.

Let me break down what that actually looks like.

The LinkedIn Proof

The best part of my job right now? Watching people showcase their AI experiments on LinkedIn.

The marketing manager who posted about building a competitive research workflow that cut her time from 6 hours to 45 minutes. On a Saturday. 

The finance analyst who automated the month-end close and stopped working weekends. Built it during a slow week. No budget approval.

The customer success lead who created an AI triage system handling 80% of support tickets. Weekend project.

I just saw a fellow recruiter and new father post about testing Clawdbot to create an Agentic AI sourcer in between diaper changes. 

These people are inspiring.

Not because they're technical geniuses.

But because they didn't wait for permission.

Nobody mandated it. Nobody put "AI innovation" in their job description. Nobody gave them a training budget.

They saw repetitive work. Got curious. Experimented on their own time. Built something. Shipped it.

That's AI-native.

The Bottom-Up Revolution

Here's the pattern I'm seeing across almost every company I work with:

AI transformation is happening bottom-up, not top-down.

The CEO didn't issue the decree, "We're now an AI company." (cue Michael Scott)

Individual contributors are just... starting to build.

And the smart companies are noticing. Promoting them. Letting them scale what they built.

Creating new roles around them.

What This Looks Like in Practice

Example 1: Sales Operations Analyst → Director of Revenue Intelligence

Before: Spent 4 hours creating custom dashboards. Pulled Salesforce data manually, cleaned it in Excel, built charts. Did this 3-4 times per week.

What they built (weekend project): Used ChatGPT to write Python scripts automating the entire pipeline: pull → clean → visualize. Dashboards now generate in 10 minutes.

Result: Freed up 12 hours/week. Used that time to build predictive models for pipeline health. Became indispensable. Got promoted and asked to build out an analytics function.

Salary progression: $95K → $195K in 14 months.

Example 2: Marketing Manager → Director of Market Intelligence

Before: Spent 6 hours/week manually researching competitors—visiting websites, compiling screenshots, building comparison slides.

What they built (self-taught, no approval needed): Claude-powered workflow that scrapes competitor sites, extracts positioning/messaging, generates comparison tables. Request to

deliverable: 45 minutes.

Result: Became the go-to competitive intelligence source. Created so much value the company built a new function around her.

Salary progression: $110K → $180K in 11 months.

Example 3: Finance Associate → Manager of Financial Automation

Before: Month-end close took 6 days. Worked every weekend during close week. Manual reconciliation, error-prone, miserable.

What they built (during a slow week): Excel macros (written with ChatGPT's help) that automate variance analysis. Built AI-powered reconciliation checker that flags anomalies. Close now takes 2.5 days.

Result: Stopped working weekends. Used extra time to build forecasting models. Tapped to lead automation across Finance.

Salary progression: $75K → $150K in 18 months.

The Pattern: Domain Expertise + AI = Force Multiplier

Notice what these people have in common?

They're not engineers.

They're domain experts who learned just enough AI to solve their own problems.

The Marketing Manager didn't need to understand LLMs. She needed to know what makes competitive analysis valuable.

The Sales Ops Analyst didn't need a CS degree. He needed to know what "good sales data" looks like.

The Finance Associate didn't need to fine-tune models. She needed to understand month-end close workflows.

They brought the judgment. AI brought the leverage.

And here's the critical insight:

The hardest part of AI isn't the AI. It's knowing what problem to solve.

The domain expertise is the moat. The AI is the force multiplier.

The Shift I'm Seeing in the Market

6 months ago: Companies wanted "AI Engineers" and "ML Scientists."

Today: Companies want "Finance people who use AI" and "Marketers who build with AI" and "Ops leaders who automate with AI"

These aren't "AI roles."

They're traditional roles turbocharged with AI.

And the people getting them aren't the ones with the most AI credentials.

They're the ones who shipped something that proved they could use AI to multiply their impact.

The Two Classes Emerging

We're heading rapidly toward two distinct classes of workers.

Class 1: The Fluent

People who instinctively look for leverage. Who see repetitive work and think "I could automate this." Who experiments on weekends because they're curious.

They're not smarter. They're not more technical. They just started earlier.

Class 2: The Left Behind

People waiting for their company to "figure out AI" before they start learning. Waiting for training. Waiting for permission. Waiting for AI to be "part of their role."

The gap is widening. Fast.

The Fluent are getting promoted. Getting recruited at 2x their current salary. Building teams. Defining new roles that didn't exist 6 months ago.

The Left Behind are wondering why they're being passed over for promotions. Why their output seems less valuable. Why younger people with less experience are suddenly making more money.

By the time companies mandate AI training, the Fluent will be two years ahead.

And in AI, two years might as well as a decade.

This isn't about access to tools. ChatGPT is free. Claude is free. The barrier isn't cost.

This is about mindset.

Do you see AI as something to experiment with on a Saturday morning?

Or something to wait for official training on?

That choice is determining careers right now.

What Hiring Managers Should Actually Ask

If you're hiring for "AI-native talent," stop using that phrase until you can answer this question:

"What does success in this role look like if the person leverages AI vs. if they don't?"

If you can't articulate the difference, you don't need AI-native talent. You just need someone good at the job.

But if you can say:

"Success without AI: they produce 10 market analyses per month"

"Success with AI: they produce 50 analyses per month and spend the freed-up time on strategic recommendations that drive product decisions"

Then you know what you're hiring for. And you can evaluate it.

Here's what to ask candidates:

"Have you used AI to make yourself 2x more productive in your current role?"

Not "have you worked with AI."

But "have you used it to fundamentally change how you work?"

If yes: Show me what you built. What you learned. What broke. What you'd do differently.

"When should you NOT use AI?"

The right answer is specific, opinionated, and learned through failure.

Wrong answers:

• "AI can do everything"

• "I'd have to think about that"

Right answers:

• "Don't use AI for final contract review,it misses context-specific edge cases that cost you deals"

• "Don't use AI for executive communications without human review, the tone needs to be perfect and the stakes are too high"

• "Don't use AI for cold outreach without customization, it's too generic and burns your prospect list"

These come from experience, not theory.

What This Means for Your Career

If you're waiting for your company to become AI-first before you start experimenting, you're already behind.

Here's my recommendation:

This week: Pick one repetitive task in your current role. Something that makes you think "ugh, I have to do this again?"

This weekend: Spend 3 hours trying to automate it with AI. Use ChatGPT, Claude, whatever. It doesn't have to be perfect. It just has to work. Go down a Youtube rabbit hole if you have to.

Next week: Document what you built. Write a LinkedIn post. Include:

• The problem

• What you built

• What you learned

• The results (time saved, errors reduced, anything measurable)

Repeat.

In 30 days, you'll have 4 case studies.

Four examples of "I saw a problem, built a solution, shipped it."

That's more than 90% of people applying to AI roles can say.

Now you're AI-native.

Not because of your title. Because you did the work.

The Future Is Already Here

We're not heading toward a world where "AI jobs" and "non-AI jobs" are separate categories.

We're heading toward a world where every job has an AI-enabled version and a non-AI-enabled version.

And the AI-enabled version is:

• 3x more productive

• 2x more valuable

• 10x harder to replace

The people figuring this out in 2026 will be running departments in 2027.

The people waiting for their company to mandate it will be wondering why they got passed over.

This isn't 5 years away.

This is happening right now.

The opportunity is open to everyone and most of the tools are free.

My Challenge to You

Pick one repetitive task you do this week.

Spend 2 hours this weekend building an AI solution for it.

It might not work. It might be terrible.

But you'll learn more in those 2 hours than in 6 months of reading about AI.

Then share what you built on LinkedIn. Inspire someone else to start.

That's what AI-native actually means.