Where to Source AI-Fluent Talent: Strategies for Early Career Teams

The talent you’re trying to hire for AI-enabled roles likely isn’t living on your target school list. It’s probably not in the clubs your campus team visits. It’s unlikely to surface through the same channels that worked three years ago. Most teams are still using early career sourcing strategies built before AI fluency became a hiring priority.

Something I hear constantly from tech talent leaders right now is that the early-career candidate they’re most excited about is incredibly hard to find. This is someone who can build AI agents, brings a portfolio of things they’ve actually made, and demonstrates real judgment about when to trust a model and when to push back. Finding that candidate can feel like searching for a needle in a haystack or trying to find the M&M I dropped in between my seat and center console in my car (we all know that’s only coming out during the next time my car gets detailed).

And then I ask, “Where are you looking?”

The answer usually sounds something like this: our target schools, our campus recruiting calendar, and maybe LinkedIn. In other words, they’re looking in exactly the same places they’ve always looked. And that’s the core of the problem: the sourcing model hasn’t caught up to the talent profile.

You can’t find a new kind of candidate using an old kind of search. The talent that defines AI fluency isn’t waiting to be discovered through your existing channels. It’s building things in places your sourcing team isn’t looking.

Where to Find AI-Fluent Talent

Some tech hiring teams are already changing where they look for AI-fluent early-career talent. Clubs and career fairs still matter, but they are no longer the center of the strategy. Recruiters are searching GitHub, hosting their own hackathons, and attending events that attract serious builders. They are scraping author names from academic papers and reviewing conference attendee lists as much as they once searched LinkedIn. They’re finding what tech talent leaders describe as “builder communities”: spaces where people are actively creating and experimenting with AI-enabled work, sharing what they’ve built, and collaborating with others.

The signal they’re chasing isn’t a degree or a GPA. It’s tangible work that demonstrates what a candidate can actually do. It’s “show us what you’ve made” – reminds me of “show me your work” on math tests growing up, i.e., prove that you really know what you’re doing. Several employers I’ve spoken with have redesigned their application process almost entirely around that question. Portfolio over resume. Demonstrated capability over institutional affiliation.

That changes how sourcing teams need to think about candidates. If you’re looking beyond degrees, GPAs, and other traditional signals, your sourcing team needs to be clear on what AI fluency looks like across different roles and how to recognize it when they see it.

In practice, that could mean:

• From target school lists → to GitHub profiles and open-source contributions
• From GPA and transcripts → to portfolios of built projects
• From club leadership → to hackathon participation and wins
• From internship brand names → to presence in builder communities
• From LinkedIn headlines → to what candidates have actually shipped
• From campus career fairs → to conferences, academic papers, and AI-specific events

Can You Find AI-Fluent Talent on Campus?

Now, I’m not saying you should abandon campus entirely, and frankly, neither are most of the teams doing this well. Campus still matters, just differently than it used to. Most early-career recruiting teams are maintaining a campus presence specifically for brand awareness, keeping their names in front of students even if the actual sourcing is happening elsewhere.

That’s a reasonable strategy, but it requires being clear-eyed about what campus is and isn’t doing for you. If you’re still relying on campus as your primary sourcing channel for AI-fluent talent, you’re probably going to miss most of the candidates you actually want. The ones building seriously with AI, the ones your hiring managers are most excited about, are often not the ones showing up to career fairs. They’re heads-down on projects and active in communities that don’t map neatly to your campus recruiting calendar.

There’s also the question of which campuses you should prioritize, especially as AI is changing how employers think about school selection. The instinct to stick with your target school list is understandable: the relationships are established, your brand is recognized, and the pipeline is familiar. But is it the RIGHT pipeline? If you’re optimizing for AI fluency, your target school list may be working against you. A student at a less prestigious school who has spent two years building AI tools may be a stronger hire for certain roles than a student at a top-ten university who has never worked with them. When AI fluency is the priority, school prestige may not tell you much about what a candidate can actually do. That raises the question: “Does school prestige correlate with the strongest AI-fluent hires?” I personally don’t think so.

Rethinking your target school list?

Explore Rethinking School Strategy in 2026 for a deeper look at how leading employers are evaluating school selection, strengthening talent pipelines, and making more defensible recruiting investments.

Download the Rethinking School Strategy in 2026 Guide →

Campus Recruiting: Brand Building vs. Talent Sourcing

The employers doing this well are starting to manage these separately, maintaining a campus presence for brand awareness while building entirely different channels for actual sourcing. Many recruiting teams are still conflating the two, which means neither is working as well as it could.

How to Define and Assess AI Fluency by Role

Here’s the piece that gets skipped most often, and it’s the one that makes everything else harder: most teams (in fact, I don’t know of a single one) haven’t actually defined what AI fluency means for their specific roles before they redesign their sourcing.

“AI fluent” is not a job requirement. It’s a category. The AI skills that matter depend on the role. What does AI fluency look like for a recruiter on your team versus a product analyst versus someone in finance operations? The answer is genuinely different, and if your sourcing team doesn’t know the specific answer for the role they’re filling, they can’t evaluate for it, no matter how many new channels they’re using.

The teams doing this well have done the work upstream. They’ve sat with hiring managers and gotten specific: not “we want someone AI-fluent” but “we want someone who can take a raw data output from our internal tool, identify where it’s wrong, and know what to do about it.” That specificity changes what you’re looking for in a portfolio, what you ask at a hackathon, and how you assess AI skills in an interview. Without it, you’re sourcing for a vibe. And I’m all about “good vibes,” but you can’t bank on a “vibe” when making an incredibly important hire.

Questions to Audit Your AI Talent Sourcing Strategy

Before changing your sourcing strategy, ask:

• Have we defined AI fluency specifically for each role we’re hiring for – or are we using it as a general filter?
• What percentage of our actual hires last year came from our top campus channels? Is that still where this talent lives?
• Are we present in any builder communities, hackathons, or off-campus spaces where AI-native candidates congregate?
• Does our screening process create a real opportunity for non-traditional candidates to show what they can do?
• Are the sourcing signals we’re using – school, GPA, prior internships – still the best proxies for the capabilities we actually need?

How to Source AI-Fluent Talent Beyond Traditional Recruiting Channels

If I’m talking to a talent leader who wants to get ahead of this, here’s where I’d start.

Find AI Talent in Builder Communities

Get into the spaces where builders are. That means using GitHub, hackathons, and AI-specific communities as ongoing sourcing channels, not one-time initiatives, with someone clearly responsible for owning them. This is closer to how companies source senior AI researchers than how they’ve traditionally sourced early-career talent, and that’s intentional. The talent profile is converging.

Separate Campus Brand Strategy from Talent Sourcing

Keep your campus presence if it’s building your brand with students who will eventually be in your pipeline. But don’t let the campus calendar drive your sourcing decisions for roles where AI fluency is the primary requirement. Build parallel channels and be honest with yourself about which one is actually finding your best candidates. Side note: if you’re trying to put more data behind your school decisions, drop me a line! I nerd out when I get to help leaders back decisions with data.

Use Portfolios and Projects to Assess AI Skills

Redesign what a strong application looks like. If you’re still leading with resume screening, you’re optimizing for the wrong things. Build a portfolio or project component into the process early so candidates can demonstrate their capabilities rather than simply present their credentials. The teams I’ve seen do this well describe it as one of the highest-signal changes they’ve made.

Be Thoughtful About AI in Candidate Screening

Using AI tools to help manage volume is reasonable, but employers still need to decide where AI and automation belong in the recruiting process. Replacing the human, relational touch at the top of the funnel before a candidate has had any real interaction with your company is likely to narrow your pool in ways you don’t intend. This is especially true if you’re trying to reach candidates who aren’t already primed to be enthusiastic about AI-mediated hiring.

Go Deeper on AI-Fluent Hiring

What does AI fluency actually look like across different roles, and how do you assess it? In our on-demand webinar, AI Fluency Demystified, we break down how employers are defining AI fluency, translating it into hiring criteria, and evolving their sourcing and screening strategies.

Watch AI Fluency Demystified →

The early-career talent market for AI-fluent candidates is genuinely competitive right now. The companies finding the people they want aren’t doing it through better execution of the same old playbook. They’re doing it because they’ve rethought where this talent lives, what signals actually predict success, and how to build a process that surfaces capability rather than just familiarity.

That’s not a small lift. But the alternative is worse: running the same sourcing motion and wondering why you keep missing the candidates you most want.

I’ve said it before and I’ll keep saying it as long as it’s true (and it especially is right now). This job is hard. We’re here to make it easier.

Happy to dig into any of this for your specific context. It’s one of my favorite conversations to have right now.