How LinkedIn rewards tribal loyalty over truth

LinkedIn is the best and worst thing that ever happened to professional learning. It’s the largest professional network in the world. It’s where reputations are built, thought leadership is measured in emojis and visibility is often mistaken for insight. But beneath the surface lies a deeper structural flaw — one that quietly undermines the cross-functional collaboration we claim to value.

The truth is that LinkedIn is a masterclass in algorithmic inbreeding. It’s what Facebook did to our society and our politics — just dressed in professional polish.

On LinkedIn, we connect with people who think like us. We follow voices that mirror our own. The algorithm feeds us more of the same, and the cycle repeats. What looks like a professional community is often intellectual tribalism, reinforced by engagement metrics and masquerading as consensus.

Tribalism by design

The LinkedIn algorithm isn’t designed for diversity of thought. It’s built for time-on-platform and engagement velocity. That means:

• Posts with early traction get more reach.
• Engagement from people similar to you carries more weight.
• Content that provokes comment within your professional silo is rewarded.

It also means:

• Marketers talk to marketers. 
• Data scientists talk to data scientists. 
• CFOs rarely talk — but when they do, it’s not to marketers or data scientists.

None of this is accidental. It’s structural, built into the platform’s architecture, and has real-world consequences.

The death of hybrid thinking

When every function speaks its own language — and never hears another — something important dies: synthesis.

In genetics, they call it heterosis. Most of us know it as hybrid vigor — the phenomenon where diversity breeds strength, resilience and adaptability. Cross-breeding distinct lines doesn’t weaken a system; it fortifies it. It’s how nature protects against fragility.

In thinking, it works the same way. We don’t see much of that on LinkedIn. Instead, we see:

• Marketers optimizing for narrative punch, disconnected from data lag and measurement validity.
• Data scientists optimizing for precision, disconnected from operational or financial context.
• CFOs prioritizing fiduciary risk and long-term value; they are rarely looped into top-of-funnel or attribution conversations until it’s too late.

Each tribe is refining its jargon, models and KPIs — but no one creates enterprise value in isolation. That only happens at the intersections. And LinkedIn makes intersectional thinking harder, not easier.

This isn’t a takedown of marketing, data science or finance. These are my communities. They educated and shaped me. Because I care about their long-term relevance and value, I must challenge how we engage with each other and with the truth.

Dig deeper: Thought leadership: The human element your marketing needs

A personal note on tribal disloyalty

Some marketers see me as a kind of traitor — like a physician becoming a malpractice attorney. They think I’ve crossed some line by calling out systemic GTM failures and surfacing inconvenient truths about CRM corruption, marketing performance opacity or attribution theater.

The CEOs, CFOs, board directors and shareholders I talk to don’t see it that way. Function leaders who want to learn, adapt and succeed also don’t see it that way. 

They see it as clarity, accountability, advantage — and long overdue. The dissonance between these perspectives is the problem. If you view feedback from outside the tribe as betrayal instead of improvement, you aren’t in a learning environment — you’re in a loyalty cult. And cults, even well-dressed ones, don’t innovate. They stagnate.

It is happening now in a startling number of professions. People are falling on one side of the knife edge of AI or the other, not just technologically but philosophically and operationally. 

Inbred expertise is a liability

The cost of this tribalism isn’t only intellectual. It’s operational.

When GTM fails, more marketing theory won’t fix it. It needs:

• A data scientist explaining the causal lag and why your KPI correlations collapsed under scrutiny.
• A CFO quantifying the risk of investing in brand versus pipeline during economic volatility.
• A product leader assessing whether messaging and roadmap are even aligned.

But these conversations rarely happen — because the network has taught us to engage inward, not outward.

What’s the fix?

It isn’t just a LinkedIn problem. It’s a leadership problem that starts on platforms like LinkedIn because that’s where professional minds are shaped today. Here’s what we can do:

• Design for collision: Seek out voices outside your function. Comment on posts by finance leaders, engineers and data scientists — even if you don’t fully understand them. Especially if you don’t.
• Post across boundaries: Share insights framed for different tribes. Don’t just talk marketing to marketers — talk marketing to CFOs. Show your thinking in ways that map to their reality.
• Reward synthesis, not tribal loyalty: Highlight cross-functional wins. Tag peers in other departments who contributed. Normalize learning between functions.
• Be the hybrid: In every company, the most valuable people live at intersections: a marketer who understands actuarial models, a data scientist fluent in GTM, a CFO who sees brand as risk mitigation. Become that person.

Dig deeper: The 3 pillars of real thought leadership

The future belongs to hybrids

In a world of accelerating change, specialization is table stakes. Synthesis is the multiplier.

The executives who will drive transformation aren’t the deepest in any one silo. They’re the ones who can move between worlds — marketing and finance, data and narrative, vision and operational reality.

The irony? We have the largest professional graph in human history at our fingertips. But we’re stuck talking to ourselves.

That has to change — because this is the era of synthetic intelligence, not just artificial intelligence. Generative AI, analytical AI and especially causal AI don’t reward tribal expertise — they reward integration.

Causal AI, in particular, is not just a technological step forward — it’s the end of functional tribalism. It replaces silos with interlocking, heterogeneous realities and exposes not just what’s happening but why — across time, teams and tradeoffs.

In this world, no one function owns the truth. The truth is collaborative, conditional, and causally distributed.

It’s time to rewire the feed, breed resilience into our thinking, and bring back hybrid vigor — or, as the geneticists call it, heterosis. Because inbred thinking doesn’t build the future, cross-pollinated insight does.

Dig deeper: 6 tips for optimizing LinkedIn content for B2B marketing