YouTube and TikTok are, structurally, shallow content assembly lines. The algorithm isn’t a curator — it’s a conveyor belt designed to maximize retention, and retention has nothing to do with understanding. If you produce video content and feel that the more polished, deeper and more crafted it is, the less it performs — it’s not your imagination, and it’s not your fault. It’s the platform’s industrial design.
Retention metrics as industrial design
The business model of platforms like YouTube and TikTok is simple: the more time users spend watching, the more ads they see. The recommendation algorithm isn’t optimized to deliver the most relevant, truest or best-made video — it’s optimized to deliver the video that maximizes the probability of the user watching the next one.
This changes everything. In a human editorial model, the curator asks “is this important?”. In an algorithmic model, the system asks “does this retain?”. These are different questions that produce different results — and the second one systematically produces shallower content. Tarleton Gillespie describes this shift as a new form of gatekeeping where algorithms naturalize curation decisions as if they were neutral, when in fact they reflect a design optimized for scale, not for value.[1]
Dense content — content with arguments that require a pause for reflection — is penalized by retention metrics not because it’s worse, but because the average viewer consumes on autopilot, and anything that demands cognitive effort triggers earlier abandonment. The system learned that depth reduces retention, and acts to suppress it.
The algorithm isn’t dumb — it’s optimized for something else
A common mistake is treating the algorithm as an incompetent adversary that “doesn’t understand quality.” The problem is the opposite: it understands perfectly what it was programmed to maximize, and content quality simply isn’t in the optimization function.
Ted Striphas calls this “algorithmic culture”: the progressive delegation of symbolic curation — historically done by critics, editors and human curators — to computational processes that operate at scale and have no (nor need to have) criteria of value.[2] The result is that what rises isn’t the best-argued content, but what best performs within a logic that is blind to content value — as long as it retains, the algorithm doesn’t distinguish a documentary from gossip.
There’s a perverse consequence of this design that few discuss: the algorithm doesn’t flatten all content equally. It systematically flattens content that demands more from the viewer — precisely the content that builds critical thinking, offers historical context, presents multiple viewpoints or sustains a complex thesis. What survives is what demands nothing: disposable entertainment, baseless opinion, manufactured controversy.
What is lost when shallow wins
It’s not an exaggeration to say that the current algorithmic curation regime is reconfiguring what “good content” means — and the criteria are shrinking. A generation growing up consuming retention-optimized videos is learning, by osmosis, that good content is content that holds attention without effort. What doesn’t hold attention is dismissed as “boring” or “too long.”
Herbert Simon’s research on scarce attention in environments of information abundance, written in 1971, has never been more relevant: when information is abundant, what becomes scarce is the attention available to process it — and systems optimized to deliver more information, without relevance filters, worsen the problem because they compete for attention instead of organizing it.[3] The difference is that in 1971 there were no computational systems capable of modulating content production in real-time to maximize this competition. Today there are. The result is a machine for producing irrelevance at industrial scale.
What is concretely lost: the ability to sustain attention over long periods, tolerance for narratives that don’t deliver immediate reward, familiarity with multi-step argumentation, and — perhaps most seriously — the perception that there is a difference between entertainment and information.
Circumstances are not destiny
This doesn’t mean all quality content is condemned to irrelevance on these platforms. It means it won’t perform “by accident,” the way shallow content does. A dense, well-structured, authorial video requires strategic planning of format, pacing and distribution that shallow content doesn’t need — exactly the difference between industrial production and authorial work.
There are counterexamples that prove the point: 40, 60-minute video essays that accumulate millions of views on YouTube. They aren’t there despite the algorithm — they’re there because dense content also retains, as long as it’s well-structured. A long documentary keeps the viewer if the narrative arc is well designed, if there is expectation, revelation, payoff. What the algorithm flattens isn’t long content — it’s poorly structured content that demands effort without offering return. The decisive variable isn’t duration; it’s reward density per minute.
But format tactics only solve the problem up to a point, because the ceiling of what you can do within the logic of ad-driven retention is limited. YouTube’s ideal viewer is the one most captive to the feed — and content that builds critical thinking, that teaches viewers to question the medium itself, works against that design.
The structural way out lies in models that decouple revenue from ad retention. Platforms like Nebula charge a direct subscription from viewers and pay creators per hour watched, not per ad delivered — meaning a well-made 40-minute video earns more than ten 8-minute videos. Floatplane and Vimeo OTT follow a similar logic. On the free side, PeerTube lets anyone or any institution host video on their own infrastructure, without a centralized recommendation algorithm. None of these platforms have YouTube’s scale — but all prove the alternative business model is viable.
This is why I chose to migrate my portfolio to Bunny Stream with self-hosted infrastructure and keep my pipeline on free software: because digital sovereignty isn’t an abstraction. It’s being able to decide that the logic of your content won’t be determined by someone else’s industrial design. Publishing outside the grip of the retention algorithm is a technical and economic decision, not just an aesthetic one — and it’s the only approach that solves the problem at its root, instead of treating it through format tactics.
The viable short-term strategy is to treat the algorithm as a distribution channel, not a composition criterion. The idea comes first; adaptation to the platform format comes after, as packaging — cuts in the right place, a hook that isn’t empty, rhythm that sustains attention without emptying content. This isn’t conceding to shallowness. It’s understanding that the medium has rules and using them to your advantage, instead of ignoring them or merely complaining about them.
In the end, what differentiates content that lasts from content that passes is what always has been: authorial point of view, research, care about what is said. That hasn’t changed. What changed is the distribution landscape, which now requires more strategic intelligence to make the dense reach those who need it — but the definitive solution lies in changing platforms, not just formats.
Frequently asked questions
Do YouTube and TikTok actually favor shallow content?
Yes, structurally. The retention-based business model rewards content that maximizes watch time, and dense content tends to demand more cognitive effort, which reduces retention. It’s not an explicit preference of the platforms — it’s a consequence of how the recommendation system is designed.
Can quality content perform well on YouTube?
Yes, up to a point — and recognizing that limit matters. With strategic format, pacing and distribution, dense content can perform well, as long-form video essays demonstrate. But the ceiling of what you can do within ad-driven retention logic is real, and content that builds critical thinking works against the platform’s design. The definitive solution lies in alternative models (Nebula, PeerTube) or self-hosted infrastructure — which decouple revenue from retention and give creators back control over the logic of their own content.
What’s the difference between shallow content and simple content?
Simple content is accessible but not empty; shallow content is accessible because it’s empty. A short video can be dense. A long video can be shallow. The difference is what the viewer takes away after watching — a new idea or just the conditioned reflex of passing time.
Why do creators keep making shallow content if it’s worse?
Because the production cost is lower and the short-term return is more predictable. Shallow content follows a formula validated by aggregated data; authorial content requires risk, research and a point of view. The industry has learned to tolerate loss of effectiveness in exchange for predictable volume.
How do I know if my content is contributing or just taking up space?
Ask: did those who watch understand it? Can they repeat the core idea? Did they come back days later still thinking about it? These questions have no answer on any analytics dashboard, but they’re the only ones that matter.
If you’re structuring a content project, use the Briefing Generator I created — guided questions that organize all the starting points before you produce. And to find the right tone for your communication, the Voice Tone Checker helps align your message before you record.
I produce videos that truly communicate, not just retain. Get in touch if you’d like to discuss your project.
— Ricardo A. B. Graça · ricolandia.com
References
- Gillespie, T. (2014) The Relevance of Algorithms. In: Gillespie, T.; Boczkowski, P. J.; Foot, K. A. (eds.). Media Technologies. MIT Press. DOI: https://doi.org/10.7551/mitpress/9780262525374.003.0009
- Striphas, T. (2015) Algorithmic culture. European Journal of Cultural Studies, 18(4-5), 395-412. DOI: https://doi.org/10.1177/1367549415577392
- Simon, H. A. (1971) Designing Organizations for an Information-Rich World. In: Greenberger, M. (ed.). Computers, Communications, and the Public Interest. Baltimore: Johns Hopkins University Press.