Metehan Yesilyurt’s SDK analysis revealed the pipeline names. We captured months of real Discover feeds to show what each pipeline actually does — volume, reach, timing, and which publishers dominate. Here’s what 42 million cards reveal about Discover’s internal architecture.

What we did

Over three months (December 2025 – February 2026), we observed real Discover feeds from hundreds of devices. The result: 42 million feed cards analyzed. We linked each card to the precise pipeline that selected it.

Some of the names were already known from the SDK, You likely saw the SDK Analysis by Metehan Yesilyurt already. What was missing: what each pipeline does in practice. How much content it selects, how many devices see it, how fast it operates, and which publishers it favors. That’s what our data reveals.

For each pipeline, we compute four metrics:

• Reach — percentage of devices that see each URL per day
• Speed — median age of articles at time of appearance
• Exclusivity — percentage of URLs unique to that pipeline
• Volume — share of total feed

Explore all 20 pipelines visually: Open the interactive explorer →

Not one algorithm — a layered system

The common assumption: Discover uses a single recommendation algorithm. Our data tells a different story: it’s a structured system with six functional layers, each with distinct logic, speed, and audience.

The six layers:

1. Core editorial — content (34.2% of volume), moonstone (7.8%, reach 9.4%), aura (8.7%, science/tech over-represented), paginationpanoptic (5.5%, scroll infrastructure), relatedcontentruby (6.7%, click-triggered related content).
2. News urgency — mustntmiss (0.5% volume but 7.3% reach, ~2x priority boost, 29% AI Overviews content) and newsstoriesheadlines (10.6% reach, Google News story clusters).
3. Trends — deeptrendsfable detects, deeptrends persists. Sequential pipeline: 27% pass rate, 21-hour delay. x.com is a trend signal source even in EN.
4. Local/geo — geotargetingstories (x.com dominates at 43.2% in EN), webkicklocalstories (hyperlocal UK/US press, 67% exclusive URLs), astria (BBC 29.3%, horse racing, astrology, Showcase).
5. Social/video — the YouTube cascade: creatorcontent (YouTube 72.4%) → freshvideos (+15h, 94% YouTube) → neoncluster (+23h, 100% YouTube, 13% reach). The cascade that doesn’t exist in French.
6. Commercial — shoppinginspiration (13.1% reach, 2.5-day lifespan) and feedads (58.4% reach — the highest of any pipeline in any language).
7. AI Overview — discover_ai_summary (1.1% of volume, 99.997% AI Overviews content, EN-only). Quality press: Reuters, New York Times, CNBC, Financial Times, Guardian.

The four EN-specific findings

The YouTube cascade: three pipelines, one content journey

This is the most distinctive feature of the English feed. Three pipelines form a sequential amplifier:

Stage	Pipeline	Content mix	Reach	Timing
1. Intake	creatorcontent	72% YouTube, 23% x.com	6.7%	T₀
2. Filter	freshvideos	94% YouTube	7.1%	T₀ + 15h
3. Broadcast	neoncluster	100% YouTube	13.0%	T₀ + 23h

At each stage, the content narrows (from mixed to pure video) and reach increases (from 6.7% to 13%). The best YouTube content is filtered, then projected to 13% of devices — broadcast-level distribution.

Growth is explosive across all three stages: creatorcontent 7.8x, freshvideos 7.2x, neoncluster 18x over three months. The video cascade is the fastest-growing part of Discover EN.

In French Discover, this cascade doesn’t exist. neoncluster has 36 hits in 3 months. The conditions — YouTube-dominant social, pure video content, broadcast audience — are only met in English.

AI Overviews have landed in Discover — but only in EN

AI Overviews — the AI-generated summary card — has been added to Discover. But only in English.

• discover_ai_summary: 1.1% of EN volume, 99.997% AI Overview content. Reuters (12.3%), New York Times (7.5%), CNBC (7.3%). Finance and space over-represented.
• mustntmiss: 29% AI Overviews content — the highest penetration of any non-dedicated pipeline.
• paginationpanoptic: 7.8% AI Overviews — even the scroll infrastructure carries AI summaries
• In French: Almost no AI Overview hits in 3 months.

The AI Overviews source club is small and elite: Reuters, New York Times, CNBC, Financial Times, Guardian. Factual, structured, financial press. AI Overviews in Discover don’t democratize visibility — they concentrate it.

feedads reaches 58% of English devices

feedads is the single most powerful pipeline by reach — 58.4% of EN devices see each ad. YouTube accounts for 53.7% of ads (video advertising dominates). Campaigns run for a median of 57 days. The ecosystem is hermetically sealed: 99.8% exclusive URLs.

For context: the highest-reach editorial pipeline (neoncluster) reaches 13%. feedads reaches 4.5x more. The EN Discover feed is heavily monetized — significantly more than French (24% reach).

The EPL exclusion

The most surprising finding. Premier League content is systematically under-represented across 7+ EN pipelines.

The affected terms: Premier League, football, Arsenal, Liverpool, Chelsea, Manchester United, Tottenham. Each shows strong negative signals in aura, deeptrendsfable, deeptrends, geotargetingstories, astria, freshvideos, and others.

The terms not affected: NFL, NBA, Olympics, rugby, cricket, Formula 1. The exclusion is specific to EPL.

The most likely hypothesis: EPL broadcasting rights and licensing constraints create editorial restrictions that propagate through the selection system. But we can’t confirm this — it’s an observation, not an explanation.

Three publisher profiles

Quality press (Guardian, BBC, New York Times)

Present in 8-10 pipelines. Guardian shows the broadest spread — top-5 in 12 different pipelines, from content and aura to newsstoriesheadlines and deeptrends. BBC dominates astria (29.3%) and deeptrends (24.7%). mustntmiss gives a ~2x priority boost, and with 29% AI Overviews content, AI Overviews-readiness is now a competitive advantage for quality press in EN.

Tech/review site (TechRadar, Tom’s Hardware)

shoppinginspiration: 13.1% reach, 2.5-day lifespan — a strong visibility window. But shopping is a silo: low co-occurrence with other pipelines. A Samsung Galaxy S25 review stays in shopping.

The opportunity: diversify beyond pure product testing. aura over-represents science/tech content by 2-2.4x. Adding editorial context — a trend analysis, a market comparison — can open doors to aura and content, breaking out of the shopping silo.

Video-first publisher (YouTube channels)

The cascade is a 3-stage amplifier. neoncluster at 13% reach is broadcast-level distribution. The content that makes it through: news/politics (WION, NBC — 46% international news), science, and current affairs. Entertainment and gaming are present but don’t dominate.

For a YouTube creator focused on news/politics/science, Discover’s cascade is one of the most powerful organic distribution channels available — and it’s growing at 18x in three months.

Full per-profile recommendations (quality press, tech, video, local, lifestyle, finance, pure player) will be published in our Substack series.

Explore further

This article is an overview. The complete analysis — 20 pipelines, per-pipeline data, domain leaders, typical titles — is available:

• Interactive explorer: navigate all 20 pipelines, compare metrics, see top domains and typical titles
• EN Substack: weekly deep-dives with data, charts, and recommendations
• Full reference: 1492.vision — the complete pipeline-by-pipeline analysis, with 3 detailled articles.

The Discover system evolves. These findings are a snapshot from December 2025 to February 2026. The video cascade that didn’t exist in December already represents 13% of EN reach in February. Monitoring the evolution — not just photographing a moment — is where the real advantage lies.

Data: 42 million Discover cards, December 2025 – February 2026. Analysis: 1492.vision. Credit to Metehan Yesilyurt for the Google SDK analysis — our data shows what each pipeline does in practice.