Ever feel the world isn't fair? The Power Law Distribution on YouTube, Only Fans and the New Creator Economy.
Ever feel the world isn't fair? The Power Law Distribution on YouTube, Only Fans and the New Creator Economy.
And why this will be even more important in the Fifth Industrial Revolution
It was nice to be asked on Radio Scotland to discuss OnlyFans as their resident AI and tech expert. The thinking behind it inspired this blog all about the Power Law Distribution on YouTube and the Creator Economy and what this has to do with the Fifth Industrial Revolution.
You see YouTube’s creator ecosystem exhibits a classic power law distribution of success.
In simple terms, a very small fraction of creators command a hugely disproportionate share of views and revenue, while the vast majority receive only a sliver of the attention and earnings This phenomenon – often referred to as the Pareto principle or “80/20 rule” – means most of the value accrues to the top of the hierarchy.
This blog by keynote speaker Dan Sodergren, explores how this power law manifests on YouTube in terms of earnings and viewership concentration, the role of YouTube’s algorithms in amplifying a small minority of creators, comparisons with a similarly skewed platform (OnlyFans), and the broader implications for the future of work under platform capitalism and the Fifth Industrial Revolution.
Earnings Concentrated at the Top on YouTube
On YouTube, creator earnings are heavily concentrated among a tiny elite.
A venture analysis noted that the top ~0.5% of creators capture a high degree of audience engagement and income, reflecting a stark inequality among creators. In practice, only an exceedingly small percentage of channels ever earn substantial money:
- Fewer than 1% monetize:
- Out of over 30 million YouTube channels, only about 0.25% ever meet the threshold to monetize content (typically 1,000+ subscribers and other criteria) In other words, 99.75% of channels earn effectively nothing from the platform.
- Top tier incomes:
- The top 0.1% of YouTubers (channels with over 1 million subscribers) represent the highest-earning tier. These creators can make over $50,000 per month (>$600,000 per year) from YouTube ads and related monetization A handful of superstar channels (often backed by teams and production budgets) earn multi-millions annually, but they are exceptionally rare.
- Long tail of low earners:
- By contrast, the vast majority of creators earn trivial amounts. Roughly 88% of channels have under 1,000 subscribers, often translating to at best a few dollars per month or nothing at all Even creators with 10k–100k subscribers (the middle tier) typically earn only a few hundred to a few thousand dollars monthly, which is far from a full-time income
In sum, YouTube’s revenue is skewed toward the top 1%. This pattern isn’t unique to YouTube: for example, on Twitch (a live-streaming platform), the top 1% of streamers capture over 50% of all revenue.
Such statistics highlight that a small minority of creators earn the majority of platform payouts, while the “long tail” of millions of other creators compete over the remainder. If YouTube’s total advertising payouts (over $30 billion in the past 3 years) were evenly divided among all active creators, it would amount to only about $20 per creator per month – underscoring how only the top performers derive significant income, and most others see negligible rewards.
Distribution of Viewership Across Creators
A parallel power-law pattern emerges in viewership and audience attention on YouTube. Popularity is highly unequal: a small number of channels and videos get the bulk of the views, while the vast majority of content struggles for an audience.
Empirical analyses of user-generated video platforms have found that viewership follows a Pareto-like concentration. For example, one study showed that roughly 10% of the most popular videos account for nearly 80% of all views, whereas the other 90% of videos share the remaining 20% of views. In other words, a few viral hits dominate total watch time, and the long tail of millions of videos each get only a trickle of views.
This uneven distribution is also evident at the channel level. A 2021 analysis of YouTube channels noted that “subscribers and views are very unequally distributed, with a small number of channels accounting for most of the views”
The inequality can be quantified by a Gini coefficient (where 0 means perfect equality and 1 means extreme inequality). In that sample, the Gini coefficient for subscribers was measured around 0.82–0.89, indicating an extremely skewed concentration of subscribers in just a few channels. (For context, a Gini of 0.82 is higher than the income inequality of any country on Earth.)
Similarly, other platforms exhibit high inequality: TikTok, for instance, has an estimated Gini ~0.93 for video views distribution, meaning virtually all attention goes to a tiny fraction of viral content. These numbers reinforce that on YouTube, a small minority of creators and videos capture the lion’s share of audience attention, while most creators reach relatively few viewers.
YouTube’s Algorithms and the “Winner-Takes-All” Effect
YouTube’s recommendation algorithm is a key factor that amplifies the success of a small minority of creators. The platform’s algorithms are designed to maximize viewer engagement and watch time, and they learn from user behavior at scale. In practice, this means that content which is already performing well tends to get promoted even more, creating a rich-get-richer feedback loop.
As one analysis explains, the recommendation system uses signals like prior engagement: “if 1 million others… already watched, liked, and commented on a video, the system is much more certain you will too… This can lead to the ‘rich gets richer’ phenomenon.”.
In essence, once a video or channel starts gaining traction, YouTube’s algorithm is more likely to keep pushing that popular content to additional users, rather than exposing viewers to the vast sea of lesser-known videos.
This dynamic yields a winner-takes-all outcome. As the same analysis notes, if the algorithm finds that more total watch time can be gained by recommending one hugely popular video to millions of users (instead of showing each user a different creator’s video), it will keep showing that one hit video to everyone)
This is not hypothetical – it’s exactly how platforms operate today. The net result is that a few videos (and their creators) can snowball to enormous view counts, while the long tail of content remains obscure. YouTube’s algorithm, optimized for overall user satisfaction and ad revenue, has little incentive to give smaller creators equal visibility if a big creator’s content is empirically more engaging.
In fact, platforms openly measure success by aggregate watch time and ad clicks, not by equitable distribution of traffic. As long as concentrating attention on superstars yields more total viewing, the algorithm will favor that path.
In summary, YouTube’s recommendation engine tends to reinforce popularity, which boosts a small minority of creators into massive success while making it hard for newcomers to break out. This algorithm-driven inequality aligns with research showing that algorithmic curation leads to “unequalizing returns closer to highly concentrated capital income… rather than labor income”.
In other words, the rewards on YouTube more resemble a jackpot or venture capital model – big wins for a few – than a steady income one might expect from traditional labor.
YouTube vs. OnlyFans: Income Inequality and Platform Dynamics
Distribution of creator earnings on OnlyFans (monthly revenue per account). Most creators earn near $0 (left side), while a tiny fraction in the far right tail earn very large amounts. This extreme inequality is a hallmark of the creator platform economy.
When comparing YouTube’s creator economy with OnlyFans, we see similar patterns of income inequality, albeit under different platform dynamics. OnlyFans, a subscription-based content platform (popularly known for adult content but also used by fitness, music, and other creators), is even more concentrated in terms of earnings than YouTube by some measures.
Recent data reveal a staggering divide:
The top 0.1% of OnlyFans creators earn about 76% of all revenue on the platform.
On average, this elite 0.1% is making on the order of $150,000 per month each. Even the next tier – the rest of the top 1% – earns far less (around $34,000 a month on average)
The top 10% of creators collectively take home over 70% of all OnlyFans income, whereas many others earn almost nothing.
In fact, the median OnlyFans creator was found to make only about $180 per month, and a huge number of creators make <$50 a month, effectively operating at a loss after accounting for time and effort. This mirrors YouTube’s situation where only a tiny minority can live off their channel income.
Despite these similarities in outcome, the platform dynamics of YouTube and OnlyFans differ in ways that contribute to their power-law distributions:
- Discovery Algorithms vs. Self-Promotion:
- YouTube leverages a powerful recommendation algorithm that can propel content to a wide audience (though primarily for those that already show promise, as discussed). OnlyFans, by contrast, has no built-in discovery or recommendation feed for finding creators; users generally must already know a creator to subscribe or find them via external links.
- This means OnlyFans creators rely on external social media promotion and personal marketing to gain subscribers. “Without a built-in discovery system, new and smaller [OnlyFans] accounts often go unnoticed,” making it extremely hard for an unknown creator to gain traction. In other words, YouTube’s algorithm picks winners (often amplifying inequality), whereas OnlyFans offers no algorithmic help at all, arguably an even steeper climb for newcomers.
- Monetization Model:
- YouTube’s revenue primarily comes from advertising, with creators receiving a 55% share of ad revenue on their videos (and YouTube taking 45%). OnlyFans operates on a direct-pay model: fans pay subscriptions or tips to creators, and the platform takes a 20% cut, leaving 80% to the creatorfoxy.ai.
- In theory, OnlyFans’ model lets any creator monetize a small dedicated audience (even without millions of views), whereas YouTube requires at least moderate view volumes to generate ad revenue. In practice, however, both models result in a top-heavy income distribution. On YouTube, advertisers favor content with massive view counts (leading to outsized ad earnings for top channels).
- On OnlyFans, a small number of creators attract the vast majority of paying customers – for example, reports show only ~4.2% of OnlyFans users actually pay for content (the rest consume free previews), and an ultra-small segment of “whale” subscribers (the top 0.01% of fans) contributes over 20% of all subscriber spending.
- This means a OnlyFans creator who can tap into these big spenders will earn exponentially more than one who cannot. The net effect is similar to YouTube: a few stars reap fortunes, while most others see very modest earnings.
- Content and Audience Dynamics:
- YouTube’s audience is billions of users and content is algorithmically served to viewers who often passively consume what’s recommended. OnlyFans has a smaller, niche audience where success often depends on existing fame or virality to bring followers in. Indeed, many top OnlyFans earners are celebrities or influencers (e.g. Bella Thorne, Cardi B, etc.) who migrated their fame to the platform.
- YouTube also has celebrities, but it has more examples of ordinary individuals rising to prominence via viral content. Nonetheless, the inequality outcomes converge – both platforms exhibit Pareto-like outcomes where a small percentage of creators capture most of the value
YouTube’s sophisticated algorithm can elevate unknown creators on occasion, but overall it still channels a disproportionate amount of attention to a select few. In both cases, aspiring creators face a winner-take-all market where only top performers earn life-changing incomes, and the rest struggle to monetize their work.
Broader Implications: Platform Capitalism and the Creator Gig Economy
The stark concentration of rewards on platforms like YouTube and OnlyFans carries several implications for the future of work, the fifth industrial revolution and the nature of platform capitalism:
- “Superstar” Labor Market:
- The creator economy functions on an extreme superstar model, where a tiny number of individuals capture outsized rewards while most others earn very little. Economists note that this distribution resembles the concentration of wealth or capital ownership more than it does a normal labor income spread.
- In traditional labor markets, many workers can earn middle-class incomes; in the creator gig economy, the “middle class” of creators is thin or hollowed out . One study found that platforms with algorithm-driven attention (YouTube, Instagram) had lower median and average creator earnings than those with more linear or chronological feeds, suggesting that algorithms fuel a winner-take-all dynamic at the expense of a creator middle class
- This raises concerns that as more people pursue creative careers, most will not achieve sustainable incomes. Creating online content is becoming a high-risk, high-reward occupation akin to entrepreneurship or performing arts – glamorous for the few who “make it” and precarious for everyone else.
- Precarious Gig Work:
- For the majority of creators, content creation is effectively a new form of gig work. With over 40–50 million people worldwide now part of the creator economy (by some estimates), the promise is that anyone can earn a living from their passion. The reality is that the vast majority cannot rely on it as a primary livelihood. YouTube’s own payout figures illustrate this harsh truth: the platform paid out around $30 billion to creators over 3 years, which sounds huge, but averaged across millions of participants.
- The YouTube payments equate to only about $250 per creator per year . Indeed, “YouTube can’t support this many full-time creators” at meaningful income levels – most creators will only ever earn coffee money, while a few become millionaires.
- Similarly, on OnlyFans, thousands of new creators join with hopes of quick riches, but many end up earning almost nothing once the top performers take the bulk of subscriber dollars . This echoes patterns in other gig economy sectors where an oversupply of labor means only a fraction get ample gigs (e.g. a few Uber drivers get the best routes and surge hours, others barely break even). The low barrier to entry on platforms (anyone can start a channel or account) leads to a flooded market of creators, intensifying competition and inequality
- Platform Incentives and Inequality:
- From a platform capitalism perspective, YouTube, OnlyFans, and similar companies have incentives that are not automatically aligned with reducing inequality. Platforms make money by attracting and monetizing eyeballs at scale. As long as overall engagement and revenue grow, the platforms have little economic reason to intervene in the unequal distribution of that revenue among creators.
- In fact, enabling blockbuster creators can be advantageous: superstar YouTubers bring in large audiences that attract advertisers, and their success stories fuel the dream that keeps millions of others uploading videos in hopes of “making it.” The “rich-get-richer” outcome is not a bug but a feature from the platform’s viewpoint, since concentrating attention on proven hits maximizes efficiency and profit. Attempts to distribute traffic or earnings more evenly (for example, by capping top payouts to subsidize smaller creators) run against the grain of the profit motive and would likely face pushback from top creators who drive much of the value
- Thus, under the current paradigm of platform capitalism, we can expect creator inequality to persist or even intensify. This raises policy and ethical questions: should platforms take measures to support a broader base of creators (through discovery algorithms that “explore more and exploit less”, or more equitable revenue-sharing models) Or is extreme inequality an inevitable trait of the digital content market?
- These debates tie into wider discussions about whether gig-economy platforms owe their workers (or creators) a viable livelihood, or simply the opportunity to compete.
- The Future of Work – Aspirations vs. Reality:
- The allure of becoming a full-time YouTuber or OnlyFans creator is now part of the zeitgeist, especially for younger generations. In surveys, many teens say they aspire to be influencers or online creators. The broader implication is a shift in career aspirations towards highly visible, independent creative work – a stark contrast to traditional employment. However, the power law outcome means this path is more analogous to pursuing stardom in sports or entertainment: lots of hopefuls, very few winners.
- This has societal implications. On one hand, the creator economy offers unprecedented creative freedom and has lowered entry barriers for creative work – anyone with a smartphone can try. On the other hand, it has introduced a new kind of labor inequality and psychological pressure. Many creators invest significant unpaid time in content production, essentially providing free labor (and content inventory) for the platform until they hit a threshold of popularity (if they ever do). This dynamic has been described as a form of “platform-dependent entrepreneurship” – creators shoulder entrepreneurial risks (uncertain pay, need to market themselves, burnout) while platforms benefit from the content and data regardless of individual creator success.
- As the gig economy expands into creative fields, policymakers and society may need to consider how to support the creative workforce, whether through alternative monetization models, education on realistic outcomes, or even mechanisms like minimum guarantees for content producers.
- The current trend suggests that without intervention, a few star creators will continue to thrive, whereas the vast majority will treat content creation as a side hustle or passion project rather than a stable job.
The Conclusion - if there truly is one...
In conclusion, YouTube exemplifies a power law distribution in the creator economy: a small cohort of top creators commands most of the viewership and earnings, while the enormous long tail of creators splits the remainder.
The platform’s recommendation algorithms, driven by engagement optimization, reinforce this imbalance by funneling attention to already-popular content. Comparatively, OnlyFans shows that this power law pattern transcends content genres and platform types – in its case, direct fan patronage still concentrates overwhelmingly in the top fraction of creators, especially given the lack of discovery tools for newcomers.
These patterns underline critical implications for the future of work: the creator economy offers new opportunities for self-employment and creative expression, but it operates under a “winner-take-all” paradigm. For every YouTube millionaire or OnlyFans star, there are thousands of others grinding for minimal rewards.
Such extreme inequality challenges the rosy narrative of “everyone can be their own boss” in the digital creator era. It instead paints a picture of platform capitalism wherein digital intermediaries capture value and reward a select few, while turning a massive pool of hopefuls into a perpetual, unpaid talent pipeline.
The sustainability of this model remains an open question. Moving forward, into this time of the Fifth Induistrial Revolution, it prompts reflection on how platforms might foster a healthier creator middle class (if at all), and how individuals can navigate the creator gig economy with realistic expectations.
Ultimately, understanding the power law on YouTube and beyond is crucial for creators, platforms, and policymakers alike – it highlights that the new digital economy of work is not a level playing field, but rather a steep pyramid in which the peak is narrow and hard to reach.
Sources and further reading...
The analysis above is supported by data and studies from
Academic research by Strauss et al. (2025) papers.ssrn.com and https://www.researchgate.net/
Creator economy analyses creatoreconomy.us
Platform-specific statistics
OnlyFans earnings distribution xsrus.com and indiatimes.com and foxy.ai.
YouTube channel metrics alanspicer.com and tubebuddy.com
Extra reading
- News18 YouTube: OnlyFans 2023 Earnings Breakdown
- KraftGeek: How to Make Money on OnlyFans
- Wikipedia: OnlyFans
- The Guardian: Bonnie Blue Documentary Review
- Evening Standard: Bonnie Blue Documentary Coverage
- The Sun: Viewer Backlash to Bonnie Blue Documentary