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.