Month: December 2025

LINKEDIN: The Logic Behind The 2026 Algorithm Pt.2

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As LinkedIn’s algorithm matures, its priorities are beginning to mirror those of AI summary and answer engines. Engagement, topic consistency, and persistence are no longer social signals but confidence indicators, ways of assessing whether an idea can be trusted, reused, and abstracted without loss. This second piece of four explores why LinkedIn now appears to think less like a feed and more like a knowledge system, and what that convergence means for professional visibility.


Part 2: Why the Algorithm Now Thinks Like an Answer Engine

Once you stop thinking of LinkedIn as a feed, the rest of the behaviour makes sense.

Engagement, for instance, has not disappeared. It’s just been reinterpreted. A like is now little more than a nod. What matters is what looks like work. Long comments. Disagreement. Reframing. People taking an idea, turning it over, stress-testing it in public.

Those behaviours aren’t “engagement” in the social sense. They are confidence signals. They answer the same question AI summary systems ask before they surface anything: can this idea survive contact with intelligence?

If it can, it travels. If it can’t, it vanishes quietly.

The same logic applies to topic consistency. The 2026 algorithm is unusually attentive to what you return to, not just what you post. It notices whether you are circling a domain or skipping across them. Whether your thinking compounds or resets.

This mirrors exactly how AI systems establish authority. They don’t crown experts based on a single performance. They infer expertise through repeated association between an entity and a conceptual territory.

Post broadly and you dissolve.
Post narrowly and you condense.

This is why generic AI content is struggling. Not because LinkedIn has developed a moral objection to machines, but because derivative material fails the summarisation test. It adds no new signal. It cannot be safely abstracted. It collapses into sameness the moment it’s removed from its original phrasing.

Machines don’t distrust AI.
They distrust redundancy.

The irony, of course, is that the more AI content floods the platform, the more valuable human specificity becomes. Experience. Trade-offs. Uncertainty. The awkward edges that can’t be smoothed away without losing meaning.

That kind of material doesn’t perform instantly.
But it persists.

Persistence is what both LinkedIn and AI systems now reward.

Tomorrow, Part 3. From Posting To Permanence.

LINKEDIN: The Logic Behind The 2026 Algorithm Pt.1

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LinkedIn’s 2026 algorithm is widely being discussed as a technical update, but that framing misses the point. What’s actually happening is an identity shift: the platform is moving away from real-time feed dynamics and towards long-term professional relevance. My four part article explores why LinkedIn no longer behaves like a social network, how persistence has replaced velocity, and why the content that now survives looks suspiciously like material designed for answer engines rather than feeds.

Part I: The Day LinkedIn Stopped Being a Feed

There was a time when LinkedIn was a feed in the old sense of the word. A stream of updates, opinions, announcements and personal reinvention, moving fast enough that yesterday’s certainty was already buried by lunchtime.

That time has passed.

What most people are calling the “2026 algorithm update” isn’t really an update at all. It’s an identity change. LinkedIn has quietly stopped behaving like a social network and started behaving like something else entirely: a professional relevance engine.

The tell isn’t reach. Reach is a lagging indicator and always has been. The tell is what persists. Posts that should have died hang around. Conversations resurface days later. Certain voices appear again and again, not because they shout, but because the platform seems oddly reluctant to let them go.

This isn’t nostalgia or favouritism. It’s structural.

The old feed rewarded motion. Frequency, velocity, visible engagement. The new system rewards something closer to stability. Ideas that hold together. Arguments that don’t collapse when challenged. Thinking that survives being returned to.

That alone should sound familiar to anyone paying attention to how AI answer engines work.

AI systems are not interested in novelty for novelty’s sake. They are interested in material that can be retrieved, summarised, abstracted and reused without distortion. LinkedIn, it turns out, is now optimising for the same thing.

Which means it’s no longer ranking posts. It’s curating candidate knowledge.

Most people are still posting as if they’re feeding a stream. The platform, meanwhile, is quietly building a library.

Tomorrow, Part 2. Why The Algorithm Now Thinks Like An Answer Engine.

AI: Search Summaries – Christmas 2025 Essay

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I’ve spent 2025 understanding Generative Engine Optimisation and AI Search Summary preeminence at an academic level. The complacency regarding this phenomenon in business is shocking, but from a behavioural psychology perspective not unexpected. Thankfully it’s not too late for your business to capitalise.


The Most Dangerous Assumption in Search Right Now

The most dangerous assumption business owners are making today is not that AI search will fail. It is that it will behave like search always has.

Those working closely with Generative Engine Optimisation already know the uncomfortable truth. Search is no longer primarily about ranking pages. It is about being recognised as a source of truth inside an answer that may never send a click at all. Yet across industries, business leaders remain curiously relaxed. Revenue still comes in. Rankings still look acceptable. Dashboards do not scream emergency.

This is precisely the problem.

AI summaries do not announce disruption with penalties or crashes. They erode relevance quietly. They absorb demand upstream. They reward authority before most organisations realise authority is being measured differently.

For years, visibility meant position. First page. Top three. Number one. The mental model was simple and it worked. Now the interface itself has changed.

‘The search engine no longer asks users to choose. It decides, synthesises and presents a conclusion. If your brand is not present in that synthesis, you are not competing. You are absent.’

Many business owners struggle to internalise this because absence is invisible. There is no warning light for being excluded from an AI-generated answer. Traffic does not collapse overnight. Leads taper slowly. Performance reviews become conversations about seasonality, budgets or market conditions. The real cause remains unseen.

Complacency is reinforced by past success. If traditional SEO, paid media and brand recognition have delivered growth for a decade, it feels reasonable to assume they will continue to do so. That assumption is understandable. It is also historically naïve. Every major platform shift has rewarded early adopters and punished those who waited for certainty.

There is also a deep misunderstanding about what AI systems value. Many businesses believe that being good at what they do is enough. Decades of experience. Strong client relationships. Industry reputation. None of this automatically translates into AI authority.

Generative systems privilege clarity, consistency and structure. They reward entities that are easy to understand, easy to verify and easy to cite. Expertise that lives only in people’s heads, sales conversations or poorly structured content might as well not exist.

This is confronting. It implies that real-world authority is not sufficient. That uncomfortable implication is often dismissed rather than addressed.

‘Another factor is fatigue. Business leaders have lived through years of algorithm updates, platform volatility and digital false dawns. Each new shift sounds like noise until it becomes unavoidable. AI summaries are therefore filed mentally alongside blockchain, voice search or the metaverse. Interesting, perhaps important one day, but not urgent.’

The flaw in that thinking is scale and intent. AI summaries are not an experiment at the edge of search. They are becoming the interface itself. They sit directly between demand and discovery. They collapse the journey from question to conclusion.

When experts raise the alarm, they are often ignored because they are early. Early warnings always sound theoretical. Yet AI systems do not wait for consensus. They learn continuously. They establish citation hierarchies long before markets agree they matter. In other words, they value early adoption.

So by the time AI summary inclusion is widely recognised as critical, the sources deemed authoritative will already be entrenched. Catching up will be far harder than acting now.

This is not about chasing another optimisation tactic. It is about ensuring your business is legible to machines that increasingly decide which voices are heard at all.

‘The real risk is not being outranked. It is being unrecognised.’

Unrecognised businesses do not fail dramatically. They fade quietly, wondering where the demand went, while answers are being given elsewhere.

Steve Coulter is a four decades Sales and Marketing professional and enthusiast who has embraced the Internet and e-Commerce since 1999.