Algorithmic Trauma Bonds: Hooked into a Broken Job Market
How platforms condition isolated dependency — and how community reviews restore agency.
The Hook
Have you ever stayed on a dating app long after you knew it was making you miserable?
Or kept sending job applications into the void, even when you suspected a lot of postings weren’t real?
That isn’t laziness. It isn’t irrational hope.
It’s design.
Platforms have learned how to create a level of algorithmic trauma bonds — training us to stay hooked on systems that quietly profit from our failure.
Who This Is For
Job seekers sending out hundreds of applications and hearing nothing back
Employers wondering why they have to put up road signs in an age of AI
Researchers and policymakers trying to understand why platforms like Indeed, LinkedIn, and Tinder keep growing — even as trust collapses
Anyone sensing that our online systems aren’t just inefficient — they’re psychologically shaping us
Trauma Bonds 101
In personal relationships, trauma bonds form when:
The same source causes harm and offers intermittent relief.
Validation and rejection come in unpredictable cycles.
Your nervous system learns:
“If I just try harder, I can make this work.”
The unpredictability itself hooks you. You stop tracking reality and start tracking the hope of resolution. (In behavioral science, this is the power of variable-ratio reinforcement — the “maybe the next one hits” schedule that’s hardest to extinguish. APA Dictionary)
Platforms Replicate the Same Logic
Algorithmic platforms scale this exact pattern:
You’re ghosted by 100 jobs… then you’re told you’re a “top candidate.”
You’re ignored by dozens of matches… then “Someone liked you!” pings your phone.
You invest more, thinking relief is just around the corner.
But here’s the design flaw: these platforms don’t succeed when you succeed. They succeed when you think you are about to just around that next corner. They are a slot machine for relationships. Every frustration feeds engagement. Every failed connection feeds the system.
Let’s ground this where the harm cuts deepest:
Revelio Labs: The hires-per-posting ratio fell from ~0.75 in 2018 to <0.5 in 2023 — meaning fewer than half of online postings now lead to a hire.
BLS: The hires rate averaged ~3.8% in 2023, within a tight band vs. recent years (roughly 3.8–4.3% during 2020–2023) — i.e., no commensurate surge in hiring to match posting volume.
Recruit (Indeed/Glassdoor): Revenue per paid job ad rose in Q4 FY2024 even as the number of paid ads decreased; overall HR Tech revenue fell YoY (FY2024 vs. FY2023).
Interpretation: The platform’s financial heartbeat depends less on whether a vacancy is resolved and more on how many times the same unresolved need can be monetized (resurface, sponsor, boost) — so it’s strategically useful if you believe the solution is always just around the corner.
This is trauma-bond logic in action: the more the system fails, the harder people work inside it, convinced relief is one more click, one more repost, one more upgrade away. It’s not irrational; it’s conditioning.
Intermittent likes/matches keep you coming back.
Ghosting is common, but you stay, convinced the “right one” is in the next swipe.
The platform’s incentives are misaligned with yours — it monetizes continued use, not exits.
Different market, same nervous-system hijack.
What is the mechanism?
When investors judge growth on ARR, LTV/CAC, and NRR tied to circulation (impressions, clicks, applications) rather than verified hires, founders and innovators rationally tune product and pricing toward circulation. That creates the game; variable-ratio reinforcement keeps people playing it.
Capital → KPIs (ARR, LTV/CAC, NRR)
KPIs → Pricing & Product (monetize circulation vs. resolution)
Product → UX loops (alerts, boosts, reposts, nudges)
UX → Behavior (variable-ratio persistence)
Net effect: Revenue rises with tries, not hires → perverse incentives.
The Mechanism I Use to Address It
At The Job Applicant Perspective, I’ve changed the frame entirely to support engagement while offering solutions. In my accelerator, I separated user acquisition cost from customer acquisition cost because the unit of value I’m essentially selling is trust, not engagement.
Maximizing job-seeker trust for profit → hiring outcomes + job seeking community
This is where a shifted reference frame of what HR tech should/could be addresses two major socioeconomic ills.
Community and belonging for people lost in shamed isolation from what hiring has been for 20 + years (tens of millions of isolated, shamed, capable, yet long-term un(der)employed because of the current economic model of HR tech are no civic society’s friend.)
Resolution for people looking for work or looking to hire (reducing the economic detriment from the current profit model.)
Community and Empathy actually DOES grow on trees. Offering this paradigm shift in how we look at hiring as both an economic, societal, and civic value is powerful both economically and societally. It alleviates harm related to multiple entrenched societal ills at once - while offering the possibility for something even more.
Using the agency that treating job seekers as people worth learning from could be, CREATES the stronger revenue stream opportunity to start corralling white labeled ATS systems, staffing and recruiting firms and more into my reputation as a service revenue stream. The long term goal: to spin the fly wheel of the online job market to the point of pulling the whole market into the service of its stakeholders. This lets employers choose their hiring tools the way you'd choose a restaurant for a first date: based on reviews of the actual experience, not the menu's description.
Note: For me, a human system, a great series of wheels of interlocking human engagement, should be coherent and adaptable in a way that is undeniable for those just seeing the functionality. It must bring practical immediate value regardless of whether it is fully understood by its users. But also it should be elegant, strong, with a symmetry that those who come after me (with my skillset) could recognize like an easter egg. All structures decay and shift in time, all structures need repair, physical, social, economic or otherwise - but even so when I walk older streets in Topeka, Kansas looking at decaying great houses from the late 1800s, it is magnificent for me. I know houses built with love when I see it. I mean to build my economic innovations with equivalent care.
Ruminations aside:
Obviously I wouldn’t still be at the forefront of a paradigm shift in what hiring tech is and/or should be if I’d already landed that million dollar buy-in. But I make my distinctions between existing tech and what I have created the sapling version of nonetheless.
The challenge of being a first mover is entirely accurately understood by most business advisors - education.
What We’ve Lost: Lateral Trust
In healthy marketplaces (think ride-sharing or short-term rentals), lateral reputation flows between users. In hiring and dating, trust often flows vertically into the platform.
You can’t see which employers ghost.
You can’t tell which listings are stale.
You can’t verify whether a match is real.
Opaque design keeps you dependent — and dependency is the product.
In a world where dependency is the product - for me - agency and community is the solution.
Join the Conversation, Not the Loop
If you’ve ever wished you could compare notes with other job seekers — to know which listings are real and which employers follow through — you’re not alone.
That’s why we built and promote The Job Applicant Perspective: anonymous hiring reviews and shared reputational data. Think of it like road conditions in the snowstorm of the online labor market: less wasted time, fewer dead ends, more trust. In a snowstorm you do not call the corporate office for advice, you listen to people on the road.
Check the Research
My open-access work
Theory of Online Market Gravity – Principle 6: Emotional Infrastructure as Economic Infrastructure (why witness/transparency is a market function, not fluff).
The Architecture of Dysfunction in Modern Labor Markets (structural failure modes in digital hiring).
Principle 3: The Two-Sided Trust Principle and related papers in the series.
External receipts for key claims
Intermittent rewards hook behavior (why “maybe the next swipe/app will hit” is sticky): American Psychological Association dictionary definition of variable-ratio schedule (intermittent reinforcement).
Job-board incentives (revenue tied to ads/monetization, not hires): Recruit Holdings (Indeed/Glassdoor) annual report notes revenue drivers such as revenue per paid job ad and monetization in HR Technology.
ATS creates “hidden workers” (qualified people filtered out by automated screening): Harvard Business School’s Hidden Workers: Untapped Talent.
Ghost jobs / conversion from posts → hires: Revelio Labs finds hires per posting fell from ~0.75 (2018) to <0.5 (2023).
Closing: From Dependency to Agency
These platforms were built to keep you hooked, not to help you win. Once you recognize that, the shame shifts. You weren’t “failing”; you were being conditioned.
Breaking free starts with visibility, with comparing experiences, and with building systems that answer to people again — shifting from dependency to agency, and from vertical trust in platforms to lateral trust in each other.