u/EmptySetAi

Data from ~100K independent YouTube channels (10K–1M+ subs). Each dot is a real channel with a known start date; inactive ones have a confirmed last-upload date. A channel is counted as inactive only if its last upload was more than 180 days ago, so anything still posting within the last 6 months is excluded from the "channel death" count.

What the data shows:

• The typical channel goes dark at age 8–9: that's the single most common death age across the whole dataset
• Survival curves are remarkably consistent across creation cohorts: most classes (2012–2021) still have >50% of channels posting today
• Genre predicts longevity: People & Blogs burns out at a median 7.5 years; Nonprofits & Activism lasts 11.5 years. 4-year spread just from content category
• The cohort heatmap shows both diagonal and vertical bands: channels age out naturally around year 8, but 2023–2025 lit up across all creation cohorts simultaneously, pointing to a platform-level shift, not just demographics
• The annual mortality rate went from 3 per 1,000 alive channels in 2016 → 162 per 1,000 in 2025, a 52× increase in per-channel risk over 9 years

"Isn't this just channels hitting their 8-year lifespan all at once?"

Fair question. A lot of channels were created in 2015–2017, so you'd expect a natural pile-up of deaths around now. But the mortality rate controls for that (it mostly does, it's not perfect): it measures deaths per 1,000 channels that were actually alive that year. That rate has nearly tripled since 2022 alone (57 → 162 per 1,000). Channels created in 2012 and channels created in 2019 are both dying at higher rates in 2024–2025 than in any prior year.

A note on data limitations: I have less data points for channels created before 2012, and 2012 itself is only sparsely represented. Any trends involving early cohorts should be taken with a grain of salt. That said I stand over the overall patterns are consistent enough across the better-represented years that the broader conclusions hold.

Methodology

I started with a dataset of ~100K (97,601) independent YouTube channels from 2019, then pulled refreshed data on each one via the YouTube Data API in 2026 to measure what had changed. Channels were grouped by their 2019 subscriber count into four size tiers: Nano (10K–50K), Micro (50K–200K), Mid (200K–1M), and Macro+ (1M+).

Each channel was then classified into one of six outcomes:

• Breakout: still actively posting, subscribers at least doubled since 2019 (≥2×)
• Growing: still actively posting, subscribers up 20–100% since 2019
• Stalling: still actively posting, subscribers less than 20% higher than 2019
• Fading: still posting, but averaging fewer than 2,500 views per video across their last 5 uploads, the algorithm has effectively stopped recommending them
• Dormant: no upload in 180+ days
• Gone: channel deleted, no videos present, or inaccessible via the API

Data source: YouTube Data API v3. Only independent creator channels were included: brand accounts, news outlets, and channels with fewer than 10K subscribers in 2019 were excluded.

The nature of Subscriptions on youtube means that its rare that there is significant decline in subscriptions, it's usually viewership which will decline and then that leads to a channel going dormant. I did not have average views data for 2019, so I did the best I could with the subscription data.

Personal note: my own channel falls squarely in the Stalling category and is probably headed Dormant soon. Sometimes you build the study and then realise you're one of the data points. This was done as part of CreatorMap.