Why LinkedIn Endorsements Are Fake Currency — Stop Worshipping Hollow Signals

Introduction: the ritual of clicking "endorse"

If you've been on LinkedIn for a few years, you've seen the ritual: a steady stream of one-click endorsements that pile up under a skill and — magically — appear to validate someone's competence. As an open-source practitioner working on agent frameworks and embedded in AI research communities, I (Raphael Shu) see this behavior everywhere in my feed and in the profiles of people I respect. I also see the problem: endorsements are a low-effort social token that masquerades as meaningful credibility.

LinkedIn has become the default public résumé for much of the technical world. The platform reports over a billion registered members and powers discovery for recruiters, community organizers, and collaborators [1]. That scale gives LinkedIn influence — and with influence comes the temptation to optimize for shallow metrics. Endorsements are one of those metrics: low-friction, gamable, and easily misread as a signal of technical skill.

This article explains why endorsements are a hollow currency, summarizes what evidence and platform mechanics tell us about their value, outlines the real harms they create for hiring and community building, and offers practical alternatives for candidates, recruiters, and open-source communities who want signals that actually mean something.

Why endorsements feel valuable — social proof and low friction

Endorsements work because of well-known social heuristics. They instantiate social proof: when many connections click to endorse a skill, viewers infer competence even if none of those clicks contain context or evidence. The LinkedIn UI accelerates this by offering one-click endorsement suggestions and by surfacing top-listed skills on profiles, so endorsements accumulate quickly and conspicuously [2].

There are reasons this feels useful:

• Speed and discoverability. A glance at a skills list with high endorsement counts is faster than reading a detailed resume or digging into code.
• Network effects. If your connections are trustworthy and informed, their endorsements can point to shared norms about who is competent.
• Psychological incentives. It's easy and polite to click "endorse" when prompted — a small reciprocal kindness that scales into inflated signal counts.

But these same features are precisely what make endorsements fragile as a measure of competence. A one-click action cannot capture depth, recency, or context. It can't tell whether someone can design multi-agent coordination strategies, optimize retrieval-augmented generation pipelines, or ship production systems — the sorts of abilities that matter in AI engineering and open-source leadership.

The empirical reality: what studies and platform mechanics say

Academic and practitioner literature has repeatedly questioned the validity of LinkedIn skill endorsements as hiring signals. Researchers studying LinkedIn-based skill endorsements have found mixed results on whether endorsements influence hiring preferences meaningfully and how easy they are to manipulate [3]. Popular recruiting and career-advice publications likewise call endorsements a double-edged sword: visible and quick, but often shallow and noisy [4].

Platform mechanics matter a lot. LinkedIn's own help documentation shows endorsements are designed to be lightweight — a few clicks from connection pages and suggested skills; settings let users auto-receive endorsements and manage visibility [2]. Low friction invites low-effort behavior. Anecdotal and community reports describe endorsement-for-endorsement arrangements, network farming, and even scams that inflate apparent credibility [5][6].

Scale amplifies the problem. With over a billion registered LinkedIn members globally, the signal-to-noise ratio for any single lightweight signal falls unless it is explicitly verified or context-rich [1]. In short: endorsements are useful as broad neighborhood signals only when combined with more reliable evidence.

Sources and further reading:

• LinkedIn’s skill endorsements help page (how the feature works) [2].
• A literature review and empirical study of LinkedIn skill endorsements and hiring preferences [3].
• Practitioner write-ups and critiques of endorsements (The Undercover Recruiter) [4].
• LinkedIn guidance on scams and suspicious behavior (platform safety) [5].

Real harms: why treating endorsements as currency hurts hiring and community health

When endorsements become a proxy for hiring or community status, several negative effects follow.

1. False positives and poor hiring outcomes

Hiring decisions made primarily from shallow signals increase the chance of mismatch. A candidate with many endorsements for “Python” may still lack experience with production-grade ML pipelines, RAG evaluation, or retrieval index tuning. Recruiters who overweigh endorsements risk hiring people unproven in the tasks that matter.

1. Credential inflation and gatekeeping

If endorsements are treated as currency, community contributors without large networks (students, international contributors, marginalized groups) are disadvantaged. Success then depends on network size and reciprocity rather than merit or reproducible artifacts.

1. Erosion of meaningful reputation

When cheap signals proliferate, the ecosystem's ability to reward genuinely verifiable contributions (well-written papers, high-quality code, reproducible benchmarks) is blunted. Open-source maintainers and researchers who ship reproducible work are competing with profiles that simply aggregated clicks.

1. Gaming and fraud

Low friction leads to gaming: endorsement rings, fake accounts, and reciprocal endorsement networks undermine trust. LinkedIn itself warns about scams and manipulative behavior that aim to misrepresent credibility [5].

Taken together, these harms mean communities that prioritize endorsements risk selecting for social skill or visibility rather than technical competence, which is especially dangerous in high-stakes fields like AI agents and systems design.

Practical alternatives and better signals for AI practitioners and open-source communities

If endorsements are hollow currency, what should we use instead? Below are actionable alternatives, categorized by audience: individual contributors, recruiters/hiring managers, and community builders.

For individual contributors (how to present real credibility)

• Ship reproducible artifacts. Link to GitHub repos, demo videos, reproducible notebooks, test suites, and CI builds. For agent frameworks, include runnable examples and small integration tests.
• Ask for written recommendations, not just clicks. A short paragraph describing what you did, with scopes and outcomes, is far more valuable than a number.
• Show role-specific impact metrics. For library authors: downloads, adoption, or metrics like GitHub stars, forks, or dependency graphs. For researchers: accepted papers, code release, benchmarks, and whether your work is cited or used in demos.
• Use badges and verified contributions when possible. Foundation or project-backed badges (Linux Foundation projects, repository contribution badges) carry more context than crowdsourced clicks.

For recruiters and hiring managers (how to evaluate beyond endorsements)

• Use work samples and take-home tasks calibrated to the role (e.g., a RAG evaluation task for retrieval engineers; an agent-architecture mini-design for multi-agent roles).
• Inspect contribution history. Look at PRs, issue triage, release notes, and discussion threads to assess collaboration and systems thinking.
• Prefer contextual recommendations. A written recommendation from a manager or collaborator that names deliverables and outcomes is stronger than ten generic endorsements.
• Run technical interviews that exercise core skills. Live problem-solving and code review sessions reveal depth in ways endorsements never can.

For community builders and open-source maintainers (how to reduce hollow-signal incentives)

• Reward reproducible contributions. Highlight exemplary PRs, reproducible demos, and community runbooks rather than counting clicks.
• Use transparent reputation systems. Track meaningful contribution metrics (review count, merged PRs, triage activity) and surface those on contributor pages.
• Educate your community. Discourage reciprocal endorsement rings and encourage contextual recommendations that describe the scope and impact of contributions.

These alternatives are less frictionless than clicking "endorse" — intentionally so. Signals that require evidence scale more slowly, but they scale with trust and meaning.

Conclusion: treat endorsements as neighborhood gossip, not as proof

LinkedIn endorsements are not worthless — they are a compact, social snapshot that can directionally point to areas of expertise. But they are not currency. They are neighborhood gossip: noisy, easily gamed, and context-free. For individuals building reputation in AI and open-source, for recruiters making hiring decisions, and for communities trying to grow healthy ecosystems, the solution is straightforward: stop worshipping hollow signals and start investing in verifiable artifacts, contextual recommendations, and transparent contribution metrics.

As someone who builds open-source agent tooling and participates in multi-agent research communities, I value signals that survive scrutiny: working demos, reproducible benchmarks, and written accounts of collaboration. Encourage those signals. If you want to help candidates and community members improve visibility in truthful ways, teach them how to document their work and how to ask for meaningful recommendations — not just clicks.

References

1. LinkedIn About — "world's largest professional network" and member numbers: https://about.linkedin.com/ [accessed 2026]
2. LinkedIn Help — Skill endorsements: how the feature works and settings: https://www.linkedin.com/help/linkedin/answer/a565106
3. The roles of LinkedIn-based skill endorsements and LinkedIn-based hiring recommendations on hiring preferences — empirical study (Emerald/ResearchGate) [academic review of LSE/LHR roles]: https://www.researchgate.net/publication/375059122_The_roles_of_LinkedIn-based_skill_endorsements_and_LinkedIn-based_hiring_recommendations_on_hiring_preferences_evidence_from_Bangladeshi_employers
4. "LinkedIn Endorsements: The Good, The Bad and The Ugly" — practitioner critique (The Undercover Recruiter): https://theundercoverrecruiter.com/linkedin-endorsements/
5. LinkedIn safety and scam guidance — common LinkedIn scam tactics and how fake credibility is used: https://www.linkedin.com/top-content/networking/linkedin-professional-guidelines/common-linkedin-scam-tactics-to-watch-for/

Author note: I work on OpenAgents and participate in open-source and research communities around multi-agent systems and retrieval/RAG evaluation. My goal here is practical: replace brittle social tokens with signals that actually reflect technical contribution and competency.