You're at a cookout. You drop a small piece of chicken on the patio and forget about it.
A scout ant—brilliant little thing—finds the food, takes a bit back to its colony, leaving a scent trail as it goes. This trail signals other worker ants who follow it back to the chicken. Fifteen minutes later, that piece of chicken has 500 ants swarming it. The more ants that travel the path, the stronger the signal becomes. These are colony-specific trails, home markers that identify the path back to their own colony.
And before you know it, the chicken is gone.
The AI Pile-On Problem
Think of the chicken as a hot candidate.
We've always had the challenge of burning out top applicants—they get too many offers, then either take a job or just opt out. But it used to be that only the most talented Scouts (recruiters, hiring managers) could find the best candidates. Most couldn't do it.
Enter AI.
AI works differently than even the best human Scout. It matches a pattern, reinforces its internal logic, and then measures the "reward signal" once it learns the match is successful—reinforcing the cycle, improving itself. Human reinforcement learning (RLHF), either inferred by favored actions or by ranking model responses, further strengthens this match.
The result? An unregulated AI matching system finds talent "it knows about" (the chicken) and then the Scout pile-on begins. Every recruiter, every hiring manager, will repeat the same pattern match. And because the AI feeds off (sorry) the reinforcement, it keeps recommending the same profiles, strengthening the signal for future searches.
The ants are programmed. All of them. At once.
This is the nightmare scenario - through this reinforcement, the AI doesn't necessarily find the best talent—it finds a reasonable match and then creates algorithmic swarms that burn out the same small group of people who fit a narrow pattern of "good."
But What If We're Asking the Wrong Question?
Here's the reframe: the problem is beyond too many ants finding the same chicken.
The problem is that we've only been able to see one kind of chicken.
Nobel laureate Daniel Kahneman describes a cognitive bias he calls WYSIATI in Thinking, Fast and Slow—"What You See Is All There Is." We make decisions based on the limited information available to us, and we don't adequately consider what we can't see. We assume the visible evidence represents the full picture.
Traditional recruiting tools—resumes, LinkedIn profiles, keyword matching—create what Kahneman would call severe information constraints. They can only surface a narrow pattern of "good." The preferred company names. The desired job titles. The ideal stated years of experience. The assigned keywords. These are sometimes called the purple squirrels.
So everyone piles on the same small group of people who fit these visible patterns, while incredible talent that doesn't perfectly match the template remains invisible.
The Scarcity Is Artificial
Organizational psychologist Tomas Chamorro-Premuzic argues in The Talent Delusion that talent isn't actually scarce—we're just terrible at identifying it. Our assessment tools miss most actual capability because they're measuring the wrong things or can't capture the dimensions that matter most.
The talent shortage isn't real. The visibility shortage is real.
When your only tool for discovering chicken is a pattern-matcher trained on what chicken is supposed to look like, you'll keep finding the same chicken. Meanwhile, there could be a thousand different flavors of excellent chicken scattered across the patio—grilled, fried, spicy, herb-crusted, perfectly seasoned in ways you've never tasted—but your tools can't see them.
Making Abundance Visible
This is what Boomband is building toward with Dossiers and richer signal matching.
Instead of Players (job seekers, career builders, networkers) being constrained to resume bullet points, they can showcase:
- How they actually think and approach problems
- Their life's work, in their own words and voice
- The perspective and curiosity that makes them excellent
- The story of why their unconventional path makes them more valuable, not less
Instead of Scouts pattern-matching on job titles and company names, they can discover:
- The creative problem-solver who doesn't have the "right" pedigree
- The mid-career professional whose unique background creates unexpected advantages
- The early-career talent whose thinking is sharper than their resume suggests
- The specific flavor of excellence that fits what they actually need
When you can see more dimensions of capability, suddenly there are a thousand different kinds of "great chicken" visible in the ecosystem.
The scarcity was artificial all along. We just didn't have the tools to see it.
The Network Effect Works Both Ways
Here's where it gets interesting. The same network effects that create pile-ons can also create discovery.
Physicist Albert-László Barabási's research on network formation shows how systems develop through "preferential attachment"—the rich get richer, the well-connected get more connected. In traditional recruiting, this meant candidates with the right pedigree got more opportunities, which gave them better signals, which got them even more opportunities.
But what if the network can attach to different signals?
When Scouts discover that a Player with an unconventional background solved exactly their problem, and that match is successful, the AI learns a new pattern. When another Scout finds value in a Dossier element that wouldn't show up on a resume, the system learns to look there. When a stay at home parent shares the amazing skills used to raise humans the AI learns again. When Players who showcase their thinking (not just their credentials) get hired and succeed, the reward signal reinforces that pattern.
The AI can learn that excellence comes in many flavors—but only if the system is designed to make those flavors visible in the first place.
What This Means for Players
If you're building your career in this new landscape:
Your unique flavor of excellence can finally be seen—but only if you make it visible. The Dossier approach isn't just about having more space to talk about yourself. It's about showcasing the dimensions of your capability that resumes could never capture:
- The project that failed but taught you something crucial
- The unconventional skill combination that creates your edge
- How you think through problems, not just that you solved them
- The curiosity and perspective that makes you different (and valuable)
The question isn't "do I fit the pattern?" The question is "can I show what makes me excellent in ways that traditional formats couldn't?"
What This Means for Scouts
If you're looking for talent:
You can finally find great talent, not just the "obvious" talent. But it requires looking beyond the patterns that worked in the past.
The best candidate for your role might not have the expected background. They might have a better background—one that brings fresh perspective, unconventional problem-solving, or a unique combination of capabilities that creates unexpected value.
AI can help you discover these candidates, but only if the system is designed to surface abundance rather than amplify scarcity.
The Mirror: Players Finding Great Opportunities
The pile-on problem works both ways.
Just like Scouts swarm the same "hot candidates," Players pile on the same "hot companies." Everyone wants to work at Google, the buzzy startup that just raised a Series B, the household name brand. The WYSIATI bias hits Players just as hard—what you can see (big brands, famous companies, the startups TechCrunch covers) feels like all there is.
Meanwhile, incredible opportunities at less visible companies go overlooked. Companies doing groundbreaking work. Teams with exceptional culture. Roles with more growth potential, more meaningful impact, more alignment with what actually energizes you. They're scattered across the patio in a thousand different flavors, but traditional job search tools only show you one kind of chicken.
This is why Boomband is building Company Dossiers and opportunity signals—not just Player Dossiers.
Instead of chasing job postings at brand-name companies, Players can discover opportunities based on:
- What the actual day-to-day work looks like
- The culture and how decisions get made
- Who you'd be working with and learning from
- The company's trajectory and where they're headed
- How your specific skills would be applied and valued
When opportunities can broadcast richer signals, Players can find great matches, not just obvious ones. The startup no one's heard of yet that's solving exactly the problem you care about. The mid-size company where your unconventional background would be an advantage, not a question mark. The role that might not have the sexiest title but offers the growth you actually need.
Better matching works both ways. Scouts find great Players who were invisible before. Players find great opportunities that were invisible before. The abundance reveals itself on both sides.
More Chicken, Better Matches
The ant farm metaphor that Jim Durbin introduced isn't wrong—it's just incomplete. Yes, AI could create swarms that burn out the same candidates everyone can see.
Or AI could help us finally see that great talent isn't scarce. It's been there all along, scattered across the patio in a thousand different flavors, waiting for tools sophisticated enough to recognize that excellence doesn't have just one recipe.
The choice isn't between ants and no ants. It's between systems that amplify artificial scarcity and systems that reveal natural abundance.
We're building for abundance.
