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Report · data as of Oct 31, 2026

How 250k Substack recommendations are spread

Every Substack publication can recommend others; readers who subscribe see those suggestions. I read the lists of 6,392 publications that have been on a category leaderboard. Here's what the network looks like.

Live recommendations on record
250,453
Publications recommended
33,112
Recommend back
44%
between tracked publications
Stay in their category
41%
same main category

When today's recommendations started

Recommendations from the publications I read in full, by the month they started. The last 12 full months account for 23,074 of the live ones, 30% more than the 12 months before. Part of that gap is attrition: older recommendations have had longer to be removed. The busiest month was Oct 2026, with 3,217.

2023202420252026
Only recommendations still live today. One removed before I first read the list leaves no trace, so older months are undercounted. The last bar is this month to date.
Show as a table
Recommendations started per month
MonthStarted
Mar 202212
Apr 2022649
May 2022425
Jun 2022479
Jul 2022321
Aug 2022305
Sep 2022345
Oct 2022377
Nov 2022469
Dec 2022435
Jan 2023670
Feb 2023561
Mar 2023498
Apr 2023607
May 2023585
Jun 2023483
Jul 2023635
Aug 2023667
Sep 2023762
Oct 2023742
Nov 2023767
Dec 2023668
Jan 2024980
Feb 2024911
Mar 20241,034
Apr 2024834
May 2024919
Jun 2024892
Jul 2024949
Aug 2024990
Sep 20241,088
Oct 20241,080
Nov 20241,051
Dec 20241,018
Jan 20251,395
Feb 20251,515
Mar 20251,901
Apr 20251,836
May 20251,527
Jun 20251,435
Jul 20251,643
Aug 20251,451
Sep 20251,431
Oct 20251,549
Nov 20251,284
Dec 20251,407
Jan 20261,682
Feb 20261,573
Mar 20261,954
Apr 20261,871
May 20261,891
Jun 20261,646
Jul 20261,938
Aug 20261,690
Sep 20262,921
Oct 20263,217

43% of writers on the boards have 50 or more recommenders

Writers I track, by how many publications recommend them now.

none476 · 7%
1-4805 · 13%
5-9525 · 8%
10-24925 · 14%
25-49919 · 14%
50-992,730 · 43%
100+12 · 0%

The pile at 50 to 99 is partly Substack's limit: it lists only a publication's 50 newest recommenders, and I add older ones only from the full lists I read.

Most recommendations point at bigger writers

Where both sides are writers I track: 66% of recommendations go to a writer with more followers than the one recommending, 34% to one with fewer.

66% of recommendations point to a larger writer. The rest put a smaller newsletter in front of a bigger audience; the writer pages show which those are.

Which categories recommend which

Each row is where recommendations from writers in one category go, as a share of that row. The 12 categories giving the most. Read across: of all recommendations U.S. Politics writers give, how many land in each category.

Share of recommendations from each category to each category
From \ toU.S. Pol.TechnologyLiteratureWorld Pol.FinanceFashion/BeautyCultureFilm/TVHealth Pol.Food/DrinkInternationalHumor
U.S. Politics531186223
Technology2473921
Literature435159233
World Politics21213016481
Finance82722
Fashion & Beauty557623
Culture171776112224
Film & TV42535562
Health Politics715454
Food & Drink342731
International1526183262131
Humor1610438212117

Darker is a larger share. Numbers are percentages of the row; blank means under 1%. Main category is the writer's own best board, as Substack reports it.

Most recommended

Recommend the most

Method

From each publication's complete outgoing list and its 50 newest incoming recommendations, read weekly. Start dates are Substack's own. Shares and trends use only the 6,392 publications whose full lists I read, so the counts don't lean toward the newest 50. More on method. Look up any writer to see their own network: an example.