Guillaume Chaslot worked on YouTube's recommendation system as a Google engineer and left in 2013. He has since described what the system was optimizing and what that optimization produced.
The metric was watch time.
The reasoning was defensible. Clicks reward misleading thumbnails; watch time rewards content people actually consume. It aligns the system with the user's revealed preference rather than with their momentary curiosity, and it was, at the time, understood as an improvement.
What a system maximizing watch time discovers, over billions of trials, is what holds human attention. It does not have a theory. It runs the experiment.
What it finds is that arousal retains. Outrage, grievance, threat, conspiracy, novelty, and content that promises resolution and defers it. Negativity bias — the asymmetric weighting of negative information — means that threatening material is attended to more thoroughly, and the system reads that as engagement and supplies more of it.
Chaslot built AlgoTransparency to sample recommendations at scale, and his data through the 2010s indicated that recommendation chains tended toward increasingly extreme content, particularly on political and conspiratorial topics.
The academic literature is genuinely contested. Several careful studies using real user data have found that the recommendation system is not the dominant driver of extreme consumption, and that subscription and off-platform referral matter more; YouTube made substantial ranking changes from 2019 onward that reduced recommendations of borderline content. The strong rabbit-hole claim is not established as well as its popularity suggests, and this guide should say so.
What is not in dispute is the structural point, and it is the one that generalizes.
An optimization target selects for whatever satisfies it, including properties nobody chose. Nobody at YouTube wanted a radicalization engine. The objective function did not contain the concept.
And the loop is invisible to the user, which is the part that matters for this guide. Each recommendation is validated by the user's own click, and the user experiences the resulting feed as a discovery about their own taste. They cannot see the counterfactual — what they would have watched under a different objective — because it was never offered. This is confirmation bias with a machine supplying the confirmations, at a rate no human environment could produce.
Cross-ref: Defense — Auditing Your Information Diet