Let's delve into the tapestry of SLOP

In spring of 2026, I was thoroughly annoyed. As someone working on Large Language Models, I frequently visit subreddits like /r/LocalLLaMA, where people share their tips and tricks on how to run Claude Mythos on a potato. (If I’m being honest, I also lurk on /r/singularity, a place where so-called “accelerationists” try to hype up billionaires into speedrunning the end of the world. I hereby distance myself from this subreddit: it’s a guilty pleasure of looking into the abyss.) And every post I saw was just pure AI slop: half-baked ideas that were put into ChatGPT, with the answer copied and pasted without even looking at what was produced. A few were definitely some form of AI psychosis, but mostly it was lots of delves, tapestries, it’s not X, it’s Ys, all glued together with a hefty amount of em dashes.

Of course, it’s not just Reddit. You see AI-generated text on every social media platform there is: Facebook, LinkedIn¹1 Don’t get me started on LinkedIn: it was always my worst nightmare, but now it has turned into a cesspool of AI generated craziness., even YouTubers now read their AI-generated scripts (at least some of them). Spring 2026 was not the moment I noticed that the internet was overrun by AI-generated content, but it was the moment I had enough.

So I tried to tackle the problem the only way I know how: my plan was to measure the pain I was feeling, not just feel how sloppy and AI generated the posts I saw were. But I had the feeling that I was not the only one who was thinking about measuring slop, so I searched the web for projects that somehow could detect the sloppiness of text.

Superfluous Language Overuse Pattern

Detecting if text is AI slop is a conceptually different endeavor from detecting if text is AI generated in general. Good for me, because I don’t want to fail at implementing a general AI text detection system. I just want to spot the bad texts, and they have definite telltales.

My search at the time led me to a fun repository called the SLOP_Detector, where I of course learned that SLOP is short for Superfluous Language Overuse Pattern, which is what real experts on AI slop seem to call it, so therefore it has to be a real phenomenon. Checking out that repo, however, revealed that this is not the code I was looking for: the project mostly detected single words and seems to be focused on creative writing (which has other distinct problems).

Because my search did not yield a good one-size-fits-all solution to my AI slop detection problem, I started doing my own experiments.

Attempt one: everything is slop

As I was trying to learn what Marimo notebooks are, I coded up a script that pulls posts from /r/LocalLLaMA²2 I used a cool project called Arctic Shift that lets anyone crawl Reddit posts! and set myself a research question: do posts that read like AI slop perform worse on /r/LocalLLaMA than posts that do not?

With my first few data visualization cells done, I tasked myself with writing an algorithm that detects slop. Because I didn’t want to spend too much time on it, I just asked ChatGPT to write down the 40 sloppiest AI phrases for me and took that as the final list. Included were words like delve and tapestry, but also things like intricate, unlock, or utilize. Looking at it now, I think the assignment was misunderstood and ChatGPT just blurted out random words, but in good old AI slop posting manner, I didn’t look at the output and just pasted the list into my script. In order to fancify my detector, I additionally counted em dashes, calculated bullet list density, envisioned type-token-ratios by dividing unique words by total words, and determined sentence length variations. From these highly scientific measurements, I then calculated an overall slop score by min-max scaling all measurements between 0 and 1 (except type-token-ratio, which I inverted first) and then averaged these five values.

To try to answer my self-imposed research question, I tried to correlate my overall slop score with the number of comments on the post, my reasoning being that posts with lots of engagement (as measured by comments) perform better. If I remember correctly, I didn’t use the upvotes because Arctic Shift only returns an “upvote ratio” that was just 1.0 for virtually every post. The result? Nothing. Just noise. Every post was more or less slop, and the correlations didn’t show anything at all.

Looking back on the experiment now, I found a metric ton of mistakes that make the experiment worthless:

• The list of AI slop words is basically useless.
• The sentence length variance makes no sense, and even if it did, I counted high sentence variance towards AI slop (while a comment in my code just said “low variance = robotic”).
• The counts of words and em dashes do not account for post length: a long post generally has a higher slop score.
• I calculated sentence boundaries by splitting at periods. Again, in a subreddit like /r/LocalLLaMA, which is full of “Qwen3.5” and “12.7B Parameters”, this is really stupid.
• I didn’t account for CLI flags in a subreddit all about command line tools: Every llama-server --ctx-size 4096 would count as one em dash, and if you know what nvidia-smi outputs look like, you will know how royally useless this whole evaluation was.

So what did I learn? Whew, I think this is a bit like a Burn After Reading situation. But one thing is for sure: it is much easier to feel that a post is AI slop than to actually measure it!

How do you measure slop?

So now it’s October, and although attempt one was a great way to learn Marimo notebooks, it hasn’t exactly scratched my itch to understand what AI slop actually is. So once again, I created a repository and downloaded Reddit data via Arctic Shift, but this time with the help of Claude Code (which undoubtedly makes much smarter mistakes than me). I crawled mainly /r/LocalLLaMA (starting 2023) and /r/singularity (starting 2021), but also added /r/MachineLearning (starting 2019) because I wanted a subreddit that existed before LLMs were commercialized. Additionally, in order to maybe catch a glimpse of the madness that is LinkedIn, I crawled /r/SideProject (a showroom of now entirely vibe-coded projects) and /r/SaaS (a forum for business owners and SaaS experts). I had not visited either of these subreddits before this analysis, so while the data can tell us something about what is going on there, I will be cautious in interpreting their results.

I only selected posts that were at least 40 words long (removing all quotes, URLs or code) and limited the selection to a maximum of 5 posts per author per month. In total, I crawled ~1.2 million posts, of which ~460,000 were used in the analysis.

1,197,674 posts fetched → 460,317 scored (38%)

• scored
• over the author cap
• under 40 words
• no own text

r/LocalLLaMAfrom Mar 2023

10k

65k of 130k (50%)

r/singularityfrom Jan 2021

5k

23k of 116k (20%)

r/MachineLearningfrom Jan 2019

5k

38k of 203k (19%)

r/SideProjectfrom Jan 2019

50k

185k of 490k (38%)

r/SaaSfrom Jan 2019

20k

149k of 259k (58%)

20192020202120222023202420252026

Show the numbers

Subreddit	Year	Fetched	No own text	Under 40 words	Over the author cap	Scored
r/LocalLLaMA	2023	12,700	3,628	1,999	73	7,000
r/LocalLLaMA	2024	34,965	16,063	4,697	193	14,012
r/LocalLLaMA	2025	43,514	14,674	7,314	457	21,069
r/LocalLLaMA	2026	39,115	9,902	5,652	603	22,958
r/singularity	2021	3,551	3,160	132	0	259
r/singularity	2022	8,073	6,661	508	9	895
r/singularity	2023	26,725	18,812	2,305	260	5,348
r/singularity	2024	35,084	25,209	3,883	224	5,768
r/singularity	2025	26,059	14,033	4,648	921	6,457
r/singularity	2026	16,378	9,239	2,792	314	4,033
r/MachineLearning	2019	22,688	18,113	824	7	3,744
r/MachineLearning	2020	28,618	21,693	1,251	221	5,453
r/MachineLearning	2021	25,262	19,465	854	265	4,678
r/MachineLearning	2022	23,605	19,419	789	11	3,386
r/MachineLearning	2023	29,887	22,323	1,536	74	5,954
r/MachineLearning	2024	34,283	25,406	1,673	41	7,163
r/MachineLearning	2025	27,636	22,196	756	65	4,619
r/MachineLearning	2026	10,602	7,088	404	3	3,107
r/SideProject	2019	5,783	3,040	462	0	2,281
r/SideProject	2020	10,385	5,786	704	15	3,880
r/SideProject	2021	10,128	6,027	610	21	3,470
r/SideProject	2022	9,519	5,809	665	17	3,028
r/SideProject	2023	15,093	6,988	1,469	178	6,458
r/SideProject	2024	38,154	14,750	4,080	345	18,979
r/SideProject	2025	140,180	63,086	10,453	2,342	64,299
r/SideProject	2026	260,476	168,781	7,182	1,808	82,705
r/SaaS	2019	1,254	608	108	16	522
r/SaaS	2020	2,172	850	240	8	1,074
r/SaaS	2021	4,107	1,687	439	50	1,931
r/SaaS	2022	5,011	2,738	467	12	1,794
r/SaaS	2023	13,142	2,819	2,095	206	8,022
r/SaaS	2024	38,340	5,746	6,104	885	25,605
r/SaaS	2025	96,073	22,384	10,046	4,499	59,144
r/SaaS	2026	99,112	39,607	6,025	2,258	51,222

For each post, I scored 52 markers in 8 groups that go further than “just” simple word matches. The following is just an overview because explaining every single one would take way too long:

• Typography: the classics like em dash (—) and en dash with spaces ( – )³3 Beauty tip: don’t want ugly, way too long em dashes? Just use my recommended alternative: space en-dash space – an alternative you won’t ever regret typing :), but also the ellipsis character (… instead of …) and arrows (→ I don’t even know how to type that on my keyboard)
• Formatting: Markdown headers, bold Labels: at the beginning of lines (exactly like you are seeing in this very list), emoji-led lines
• Vocabulary: delve, tapestry, …landscape (like “the AI landscape…” – I will be coming back to this one later)
• Rhetoric: “It’s not X. It’s Y.”, “Let’s dive in”, “No X, no Y, no Z” (I find this one especially annoying)
• Tech Promo: “How it works”, “Zero dependencies/config”, “local-first”
• Assistant Leakage: Basic assistant fluff like “Great Question” that leaked into the post⁴4 Spoiler: I got false positives before 2023 and couldn’t find anything useful here.
• Grand theories: “Recursive Consciousness”, “My AI named itself”, “glyphs”, “truth protocols”, basically what I would call “AI psychosis”
• Human control markers: lowercase i or internet slang (“i dont know lol”).

Tech Promo is specific to what I see every day in /r/LocalLLaMA, assistant leakage was just out of curiosity, grand theories is something that I see a lot more than I would like (and is probably worth another blog post), and human control markers should be a sanity check and give me a sense of whether I’m really measuring AI slop or something else. What may be odd is that the spaced en dash I advocated for is here treated as an AI slop marker, but if we take a first look at the data itself, we can clearly see a pattern that can be safely associated with the rise of AI:

• r/MachineLearning
• r/LocalLLaMA
• r/SideProject

Smoothingmonthly3 mo6 mo

% of posts containing spaced en dash ( – )

Show the numbers

Month	r/MachineLearning	r/LocalLLaMA	r/SideProject
2019-01	0.7% (n=297)	–	3.0% (n=169)
2019-02	0.9% (n=332)	–	1.2% (n=172)
2019-03	0.6% (n=321)	–	1.4% (n=211)
2019-04	0.5% (n=367)	–	0.0% (n=172)
2019-05	1.2% (n=327)	–	1.3% (n=153)
2019-06	0.3% (n=296)	–	1.5% (n=199)
2019-07	0.0% (n=369)	–	2.7% (n=188)
2019-08	0.6% (n=311)	–	2.9% (n=175)
2019-09	1.2% (n=253)	–	1.3% (n=231)
2019-10	0.7% (n=279)	–	0.4% (n=224)
2019-11	0.4% (n=275)	–	2.3% (n=174)
2019-12	1.3% (n=317)	–	1.4% (n=213)
2020-01	0.5% (n=444)	–	1.7% (n=303)
2020-02	1.1% (n=377)	–	0.8% (n=265)
2020-03	0.5% (n=401)	–	1.5% (n=330)
2020-04	1.1% (n=538)	–	0.7% (n=416)
2020-05	1.1% (n=470)	–	1.4% (n=358)
2020-06	1.2% (n=417)	–	0.9% (n=339)
2020-07	0.8% (n=495)	–	1.2% (n=346)
2020-08	0.0% (n=473)	–	0.3% (n=319)
2020-09	0.6% (n=527)	–	1.3% (n=309)
2020-10	0.7% (n=459)	–	1.3% (n=302)
2020-11	1.2% (n=429)	–	0.3% (n=314)
2020-12	1.4% (n=423)	–	1.4% (n=279)
2021-01	0.7% (n=427)	–	0.9% (n=349)
2021-02	0.0% (n=440)	–	2.2% (n=318)
2021-03	1.3% (n=453)	–	2.1% (n=384)
2021-04	1.2% (n=428)	–	3.1% (n=322)
2021-05	0.7% (n=461)	–	1.5% (n=342)
2021-06	1.4% (n=483)	–	2.0% (n=300)
2021-07	1.1% (n=361)	–	0.8% (n=236)
2021-08	1.5% (n=396)	–	1.8% (n=228)
2021-09	0.6% (n=315)	–	1.7% (n=232)
2021-10	0.7% (n=303)	–	0.8% (n=261)
2021-11	0.7% (n=307)	–	3.4% (n=262)
2021-12	1.0% (n=304)	–	1.3% (n=236)
2022-01	1.1% (n=265)	–	0.8% (n=256)
2022-02	1.1% (n=282)	–	1.7% (n=230)
2022-03	0.6% (n=308)	–	0.8% (n=244)
2022-04	2.1% (n=290)	–	1.4% (n=212)
2022-05	0.3% (n=301)	–	3.4% (n=237)
2022-06	0.7% (n=300)	–	2.4% (n=207)
2022-07	1.5% (n=274)	–	1.1% (n=274)
2022-08	0.9% (n=320)	–	2.6% (n=272)
2022-09	0.0% (n=316)	–	1.6% (n=257)
2022-10	1.0% (n=306)	–	1.0% (n=298)
2022-11	0.5% (n=195)	–	1.2% (n=260)
2022-12	0.4% (n=229)	–	1.4% (n=281)
2023-01	0.4% (n=264)	–	0.8% (n=385)
2023-02	0.9% (n=232)	–	1.4% (n=353)
2023-03	0.9% (n=349)	1.7% (n=60)	0.3% (n=400)
2023-04	0.5% (n=376)	0.0% (n=191)	1.3% (n=387)
2023-05	1.3% (n=387)	0.2% (n=476)	2.1% (n=424)
2023-06	0.0% (n=194)	0.5% (n=557)	1.4% (n=438)
2023-07	2.2% (n=641)	0.4% (n=768)	1.9% (n=518)
2023-08	0.4% (n=689)	0.5% (n=856)	5.7% (n=592)
2023-09	0.8% (n=719)	0.4% (n=769)	3.2% (n=627)
2023-10	0.6% (n=688)	1.0% (n=888)	3.9% (n=709)
2023-11	1.0% (n=690)	0.8% (n=1017)	5.9% (n=861)
2023-12	0.8% (n=725)	0.8% (n=1418)	5.9% (n=764)
2024-01	0.3% (n=869)	0.8% (n=1330)	4.5% (n=961)
2024-02	1.1% (n=925)	0.5% (n=1339)	3.0% (n=972)
2024-03	0.6% (n=713)	1.2% (n=1196)	3.0% (n=1289)
2024-04	0.8% (n=746)	0.5% (n=1433)	3.0% (n=1414)
2024-05	0.0% (n=607)	0.3% (n=1186)	3.1% (n=1410)
2024-06	0.7% (n=591)	0.5% (n=988)	2.1% (n=1315)
2024-07	0.4% (n=488)	0.7% (n=1054)	2.6% (n=1526)
2024-08	0.2% (n=442)	0.4% (n=1063)	2.4% (n=1775)
2024-09	0.4% (n=465)	0.9% (n=1110)	4.3% (n=1901)
2024-10	0.7% (n=438)	0.8% (n=1154)	4.2% (n=2180)
2024-11	1.0% (n=481)	0.9% (n=1037)	4.7% (n=2165)
2024-12	1.0% (n=398)	0.6% (n=1122)	3.7% (n=2071)
2025-01	1.5% (n=409)	0.9% (n=1674)	5.0% (n=2429)
2025-02	3.5% (n=400)	2.2% (n=1467)	7.2% (n=2067)
2025-03	3.7% (n=434)	1.7% (n=1396)	9.4% (n=2680)
2025-04	5.3% (n=396)	2.3% (n=1391)	7.4% (n=4688)
2025-05	3.6% (n=503)	2.6% (n=1374)	8.1% (n=5462)
2025-06	3.4% (n=593)	2.1% (n=1198)	6.8% (n=5879)
2025-07	3.4% (n=436)	2.5% (n=1868)	7.3% (n=7549)
2025-08	1.5% (n=326)	1.7% (n=2445)	5.9% (n=6888)
2025-09	3.7% (n=301)	2.5% (n=1918)	5.5% (n=6125)
2025-10	2.6% (n=269)	2.0% (n=2004)	3.5% (n=6776)
2025-11	0.7% (n=292)	1.7% (n=2083)	3.2% (n=7213)
2025-12	1.2% (n=260)	2.9% (n=2251)	2.9% (n=6543)
2026-01	0.7% (n=286)	1.6% (n=2515)	2.6% (n=5965)
2026-02	0.9% (n=350)	1.5% (n=3268)	2.3% (n=6518)
2026-03	1.2% (n=418)	1.4% (n=4473)	1.5% (n=9426)
2026-04	0.4% (n=454)	0.7% (n=4400)	1.0% (n=9319)
2026-05	0.0% (n=404)	0.5% (n=1433)	1.1% (n=10080)
2026-06	1.0% (n=311)	0.5% (n=1383)	0.9% (n=9290)
2026-07	1.0% (n=287)	0.1% (n=1404)	1.0% (n=9311)
2026-08	0.0% (n=254)	0.6% (n=1875)	0.7% (n=9180)
2026-09	0.4% (n=251)	0.5% (n=1489)	0.6% (n=9494)

Interestingly, the spaced en dash has a high in /r/SideProject in 2024, only to return in 2025 in all subreddits, with almost 8% of posts in /r/SideProject containing it. But in 2026, the sightings drop sharply, and now we are back to levels that are similar to those before the great AI sloppification.

Even though markers are very good for seeing individual trends, my goal was always to detect if a post is AI slop. So in order to classify the posts, I turned to the reliable and highly scientific three-strikes model: posts that contain at least three different slop markers (excluding the human control markers and some markers I defined as weak, like “unlock” or “harness”) get flagged as slop.

So, how bad is it?

The data clearly shows the big AI slop bump, with subreddits firmly in the 10% slop post range. The lines stay pretty flat until late 2024 (the times of GPT-4o and Claude 3) and then skyrocket in 2025.

• r/LocalLLaMA
• r/singularity
• r/MachineLearning

Smoothingmonthly3 mo6 mo

% of posts with 3+ different slop markers

Show the numbers

Month	r/LocalLLaMA	r/singularity	r/MachineLearning
2019-01	–	–	0.7% (n=297)
2019-02	–	–	0.6% (n=332)
2019-03	–	–	0.6% (n=321)
2019-04	–	–	1.1% (n=367)
2019-05	–	–	0.3% (n=327)
2019-06	–	–	0.0% (n=296)
2019-07	–	–	0.3% (n=369)
2019-08	–	–	0.3% (n=311)
2019-09	–	–	0.8% (n=253)
2019-10	–	–	0.7% (n=279)
2019-11	–	–	0.7% (n=275)
2019-12	–	–	0.0% (n=317)
2020-01	–	–	0.0% (n=444)
2020-02	–	–	0.3% (n=377)
2020-03	–	–	0.7% (n=401)
2020-04	–	–	1.3% (n=538)
2020-05	–	–	0.6% (n=470)
2020-06	–	–	0.5% (n=417)
2020-07	–	–	0.0% (n=495)
2020-08	–	–	0.6% (n=473)
2020-09	–	–	0.6% (n=527)
2020-10	–	–	0.2% (n=459)
2020-11	–	–	0.0% (n=429)
2020-12	–	–	1.2% (n=423)
2021-01	–	0.0% (n=41)	1.6% (n=427)
2021-02	–	0.0% (n=38)	0.2% (n=440)
2021-03	–	6.5% (n=31)	1.5% (n=453)
2021-04	–	0.0% (n=21)	0.5% (n=428)
2021-05	–	5.6% (n=18)	0.9% (n=461)
2021-06	–	0.0% (n=9)	0.0% (n=483)
2021-07	–	0.0% (n=4)	0.3% (n=361)
2021-08	–	0.0% (n=12)	0.0% (n=396)
2021-09	–	0.0% (n=8)	1.0% (n=315)
2021-10	–	0.0% (n=22)	0.3% (n=303)
2021-11	–	0.0% (n=17)	0.7% (n=307)
2021-12	–	2.6% (n=38)	0.7% (n=304)
2022-01	–	2.6% (n=39)	1.1% (n=265)
2022-02	–	0.0% (n=49)	0.4% (n=282)
2022-03	–	0.0% (n=21)	1.6% (n=308)
2022-04	–	0.0% (n=63)	0.3% (n=290)
2022-05	–	1.1% (n=93)	0.0% (n=301)
2022-06	–	1.0% (n=105)	1.3% (n=300)
2022-07	–	1.4% (n=71)	1.5% (n=274)
2022-08	–	1.1% (n=89)	0.9% (n=320)
2022-09	–	0.0% (n=64)	0.6% (n=316)
2022-10	–	1.6% (n=62)	1.0% (n=306)
2022-11	–	0.0% (n=39)	1.5% (n=195)
2022-12	–	1.5% (n=200)	0.9% (n=229)
2023-01	–	1.5% (n=200)	0.8% (n=264)
2023-02	–	2.1% (n=195)	0.4% (n=232)
2023-03	1.7% (n=60)	2.3% (n=443)	0.9% (n=349)
2023-04	1.0% (n=191)	2.9% (n=525)	2.1% (n=376)
2023-05	0.6% (n=476)	2.7% (n=480)	2.6% (n=387)
2023-06	0.7% (n=557)	2.9% (n=379)	3.1% (n=194)
2023-07	1.3% (n=768)	3.4% (n=611)	3.3% (n=641)
2023-08	1.2% (n=856)	4.0% (n=568)	3.9% (n=689)
2023-09	1.8% (n=769)	3.6% (n=386)	2.2% (n=719)
2023-10	1.7% (n=888)	3.1% (n=286)	2.3% (n=688)
2023-11	2.1% (n=1017)	2.6% (n=586)	1.9% (n=690)
2023-12	1.2% (n=1418)	3.5% (n=689)	1.5% (n=725)
2024-01	1.4% (n=1330)	3.4% (n=525)	1.5% (n=869)
2024-02	1.2% (n=1339)	3.0% (n=806)	2.2% (n=925)
2024-03	1.4% (n=1196)	3.9% (n=1027)	0.8% (n=713)
2024-04	1.7% (n=1433)	4.7% (n=408)	1.3% (n=746)
2024-05	1.5% (n=1186)	2.2% (n=464)	2.3% (n=607)
2024-06	1.7% (n=988)	1.5% (n=337)	2.0% (n=591)
2024-07	2.1% (n=1054)	5.6% (n=268)	1.2% (n=488)
2024-08	2.4% (n=1063)	3.1% (n=229)	1.8% (n=442)
2024-09	3.0% (n=1110)	4.5% (n=357)	3.2% (n=465)
2024-10	2.4% (n=1154)	1.8% (n=381)	2.5% (n=438)
2024-11	2.8% (n=1037)	4.7% (n=339)	2.1% (n=481)
2024-12	3.2% (n=1122)	4.0% (n=627)	3.5% (n=398)
2025-01	2.8% (n=1674)	5.4% (n=819)	6.8% (n=409)
2025-02	4.1% (n=1467)	5.7% (n=651)	9.8% (n=400)
2025-03	4.1% (n=1396)	7.6% (n=473)	9.9% (n=434)
2025-04	5.0% (n=1391)	7.4% (n=609)	8.8% (n=396)
2025-05	6.5% (n=1374)	7.1% (n=688)	9.3% (n=503)
2025-06	8.0% (n=1198)	9.0% (n=535)	12.0% (n=593)
2025-07	7.8% (n=1868)	7.6% (n=556)	10.1% (n=436)
2025-08	8.1% (n=2445)	5.3% (n=676)	7.7% (n=326)
2025-09	8.5% (n=1918)	7.2% (n=318)	9.0% (n=301)
2025-10	7.3% (n=2004)	4.5% (n=308)	8.2% (n=269)
2025-11	9.9% (n=2083)	6.5% (n=402)	6.2% (n=292)
2025-12	10.1% (n=2251)	7.1% (n=422)	8.1% (n=260)
2026-01	10.1% (n=2515)	5.1% (n=311)	9.1% (n=286)
2026-02	10.3% (n=3268)	4.4% (n=432)	10.3% (n=350)
2026-03	10.1% (n=4473)	5.8% (n=433)	10.8% (n=418)
2026-04	8.4% (n=4400)	4.2% (n=354)	7.5% (n=454)
2026-05	6.1% (n=1433)	3.1% (n=327)	6.2% (n=404)
2026-06	5.9% (n=1383)	2.1% (n=331)	6.1% (n=311)
2026-07	6.1% (n=1404)	2.9% (n=381)	7.0% (n=287)
2026-08	5.0% (n=1875)	2.7% (n=375)	5.1% (n=254)
2026-09	5.0% (n=1489)	2.3% (n=831)	4.4% (n=251)

Out of all subreddits, /r/singularity has the least amount of AI slop, which is not exactly my experience, but oh well, the data does not lie. Also, looking at 2026, the AI slop index goes down for all subreddits. Does this mean AI is now used less? I don’t think so. I guess the models became less sloppy⁵5 Or the markers that detect the slop are different..

But what does all the AI-generated content do to all the wonderful and insightful posts written by biological humans?

• r/LocalLLaMA
• r/singularity
• r/MachineLearning

Smoothingmonthly3 mo6 mo

% of posts with any marker from human controls

Show the numbers

Month	r/LocalLLaMA	r/singularity	r/MachineLearning
2019-01	–	–	13.8% (n=297)
2019-02	–	–	12.7% (n=332)
2019-03	–	–	13.4% (n=321)
2019-04	–	–	15.8% (n=367)
2019-05	–	–	13.8% (n=327)
2019-06	–	–	10.5% (n=296)
2019-07	–	–	11.4% (n=369)
2019-08	–	–	14.5% (n=311)
2019-09	–	–	11.9% (n=253)
2019-10	–	–	12.9% (n=279)
2019-11	–	–	12.7% (n=275)
2019-12	–	–	15.8% (n=317)
2020-01	–	–	14.6% (n=444)
2020-02	–	–	10.1% (n=377)
2020-03	–	–	11.5% (n=401)
2020-04	–	–	11.7% (n=538)
2020-05	–	–	10.0% (n=470)
2020-06	–	–	16.1% (n=417)
2020-07	–	–	15.4% (n=495)
2020-08	–	–	14.6% (n=473)
2020-09	–	–	13.1% (n=527)
2020-10	–	–	13.9% (n=459)
2020-11	–	–	17.9% (n=429)
2020-12	–	–	13.5% (n=423)
2021-01	–	14.6% (n=41)	15.5% (n=427)
2021-02	–	31.6% (n=38)	14.8% (n=440)
2021-03	–	25.8% (n=31)	13.2% (n=453)
2021-04	–	14.3% (n=21)	17.5% (n=428)
2021-05	–	22.2% (n=18)	16.5% (n=461)
2021-06	–	11.1% (n=9)	15.3% (n=483)
2021-07	–	25.0% (n=4)	14.1% (n=361)
2021-08	–	25.0% (n=12)	17.4% (n=396)
2021-09	–	37.5% (n=8)	15.6% (n=315)
2021-10	–	27.3% (n=22)	9.9% (n=303)
2021-11	–	23.5% (n=17)	13.0% (n=307)
2021-12	–	31.6% (n=38)	14.1% (n=304)
2022-01	–	15.4% (n=39)	10.9% (n=265)
2022-02	–	16.3% (n=49)	15.6% (n=282)
2022-03	–	33.3% (n=21)	12.3% (n=308)
2022-04	–	19.0% (n=63)	15.5% (n=290)
2022-05	–	17.2% (n=93)	15.9% (n=301)
2022-06	–	14.3% (n=105)	16.7% (n=300)
2022-07	–	16.9% (n=71)	13.1% (n=274)
2022-08	–	25.8% (n=89)	17.2% (n=320)
2022-09	–	25.0% (n=64)	14.2% (n=316)
2022-10	–	19.4% (n=62)	14.7% (n=306)
2022-11	–	20.5% (n=39)	9.2% (n=195)
2022-12	–	22.0% (n=200)	11.8% (n=229)
2023-01	–	26.5% (n=200)	12.1% (n=264)
2023-02	–	27.2% (n=195)	13.4% (n=232)
2023-03	16.7% (n=60)	20.8% (n=443)	14.3% (n=349)
2023-04	20.4% (n=191)	22.9% (n=525)	12.2% (n=376)
2023-05	19.1% (n=476)	20.8% (n=480)	15.8% (n=387)
2023-06	19.6% (n=557)	21.4% (n=379)	17.0% (n=194)
2023-07	17.4% (n=768)	20.6% (n=611)	16.5% (n=641)
2023-08	16.1% (n=856)	20.2% (n=568)	16.7% (n=689)
2023-09	15.7% (n=769)	19.9% (n=386)	16.8% (n=719)
2023-10	18.9% (n=888)	23.8% (n=286)	14.4% (n=688)
2023-11	18.0% (n=1017)	21.5% (n=586)	14.6% (n=690)
2023-12	19.0% (n=1418)	19.3% (n=689)	14.6% (n=725)
2024-01	19.8% (n=1330)	19.2% (n=525)	14.5% (n=869)
2024-02	19.9% (n=1339)	22.5% (n=806)	17.2% (n=925)
2024-03	18.0% (n=1196)	23.6% (n=1027)	13.9% (n=713)
2024-04	20.9% (n=1433)	24.3% (n=408)	13.5% (n=746)
2024-05	18.9% (n=1186)	23.1% (n=464)	14.8% (n=607)
2024-06	16.4% (n=988)	18.1% (n=337)	13.7% (n=591)
2024-07	17.9% (n=1054)	20.5% (n=268)	13.5% (n=488)
2024-08	18.4% (n=1063)	18.8% (n=229)	14.3% (n=442)
2024-09	18.0% (n=1110)	18.8% (n=357)	16.1% (n=465)
2024-10	17.6% (n=1154)	21.3% (n=381)	13.5% (n=438)
2024-11	19.9% (n=1037)	21.8% (n=339)	14.3% (n=481)
2024-12	20.9% (n=1122)	19.9% (n=627)	15.3% (n=398)
2025-01	20.9% (n=1674)	21.1% (n=819)	12.5% (n=409)
2025-02	20.8% (n=1467)	21.8% (n=651)	16.3% (n=400)
2025-03	19.3% (n=1396)	26.4% (n=473)	13.8% (n=434)
2025-04	19.6% (n=1391)	20.2% (n=609)	13.9% (n=396)
2025-05	20.4% (n=1374)	21.4% (n=688)	16.1% (n=503)
2025-06	21.1% (n=1198)	21.1% (n=535)	15.2% (n=593)
2025-07	20.4% (n=1868)	24.3% (n=556)	15.4% (n=436)
2025-08	21.9% (n=2445)	20.0% (n=676)	14.7% (n=326)
2025-09	21.7% (n=1918)	19.8% (n=318)	14.3% (n=301)
2025-10	23.1% (n=2004)	22.4% (n=308)	18.2% (n=269)
2025-11	22.6% (n=2083)	15.2% (n=402)	14.0% (n=292)
2025-12	20.7% (n=2251)	18.2% (n=422)	15.0% (n=260)
2026-01	22.7% (n=2515)	14.8% (n=311)	17.8% (n=286)
2026-02	22.3% (n=3268)	14.6% (n=432)	13.1% (n=350)
2026-03	19.6% (n=4473)	20.8% (n=433)	16.5% (n=418)
2026-04	20.8% (n=4400)	19.5% (n=354)	15.9% (n=454)
2026-05	24.1% (n=1433)	17.7% (n=327)	13.1% (n=404)
2026-06	24.6% (n=1383)	15.4% (n=331)	8.4% (n=311)
2026-07	22.9% (n=1404)	17.3% (n=381)	12.5% (n=287)
2026-08	24.6% (n=1875)	20.0% (n=375)	13.0% (n=254)
2026-09	24.7% (n=1489)	21.2% (n=831)	14.7% (n=251)

The human markers stay rather constant throughout all this. This means that the relative proportion of human posts didn’t go up (reassuring, because otherwise my measurement method would probably be broken), but most importantly, it also didn’t go down (as could be expected with a flood of AI posts).

Speaking of floods of AI posts: the analysis of /r/SideProject reveals an incredible AI slop rate of over 20% in mid-2025!

• r/SideProject
• r/MachineLearning

Smoothingmonthly3 mo6 mo

% of posts with 3+ different slop markers

Show the numbers

Month	r/SideProject	r/MachineLearning
2019-01	1.2% (n=169)	0.7% (n=297)
2019-02	1.7% (n=172)	0.6% (n=332)
2019-03	0.9% (n=211)	0.6% (n=321)
2019-04	1.2% (n=172)	1.1% (n=367)
2019-05	2.0% (n=153)	0.3% (n=327)
2019-06	1.5% (n=199)	0.0% (n=296)
2019-07	3.7% (n=188)	0.3% (n=369)
2019-08	1.7% (n=175)	0.3% (n=311)
2019-09	1.3% (n=231)	0.8% (n=253)
2019-10	1.3% (n=224)	0.7% (n=279)
2019-11	1.7% (n=174)	0.7% (n=275)
2019-12	0.5% (n=213)	0.0% (n=317)
2020-01	0.3% (n=303)	0.0% (n=444)
2020-02	2.6% (n=265)	0.3% (n=377)
2020-03	0.6% (n=330)	0.7% (n=401)
2020-04	1.0% (n=416)	1.3% (n=538)
2020-05	2.8% (n=358)	0.6% (n=470)
2020-06	3.2% (n=339)	0.5% (n=417)
2020-07	2.9% (n=346)	0.0% (n=495)
2020-08	1.3% (n=319)	0.6% (n=473)
2020-09	1.0% (n=309)	0.6% (n=527)
2020-10	1.3% (n=302)	0.2% (n=459)
2020-11	1.6% (n=314)	0.0% (n=429)
2020-12	2.9% (n=279)	1.2% (n=423)
2021-01	2.3% (n=349)	1.6% (n=427)
2021-02	2.5% (n=318)	0.2% (n=440)
2021-03	2.9% (n=384)	1.5% (n=453)
2021-04	2.8% (n=322)	0.5% (n=428)
2021-05	2.9% (n=342)	0.9% (n=461)
2021-06	2.3% (n=300)	0.0% (n=483)
2021-07	3.4% (n=236)	0.3% (n=361)
2021-08	1.3% (n=228)	0.0% (n=396)
2021-09	4.3% (n=232)	1.0% (n=315)
2021-10	2.7% (n=261)	0.3% (n=303)
2021-11	1.1% (n=262)	0.7% (n=307)
2021-12	1.7% (n=236)	0.7% (n=304)
2022-01	2.3% (n=256)	1.1% (n=265)
2022-02	2.6% (n=230)	0.4% (n=282)
2022-03	3.3% (n=244)	1.6% (n=308)
2022-04	4.2% (n=212)	0.3% (n=290)
2022-05	1.3% (n=237)	0.0% (n=301)
2022-06	0.5% (n=207)	1.3% (n=300)
2022-07	2.2% (n=274)	1.5% (n=274)
2022-08	1.5% (n=272)	0.9% (n=320)
2022-09	1.9% (n=257)	0.6% (n=316)
2022-10	2.3% (n=298)	1.0% (n=306)
2022-11	3.1% (n=260)	1.5% (n=195)
2022-12	2.8% (n=281)	0.9% (n=229)
2023-01	1.8% (n=385)	0.8% (n=264)
2023-02	2.8% (n=353)	0.4% (n=232)
2023-03	2.3% (n=400)	0.9% (n=349)
2023-04	3.9% (n=387)	2.1% (n=376)
2023-05	5.4% (n=424)	2.6% (n=387)
2023-06	7.1% (n=438)	3.1% (n=194)
2023-07	6.0% (n=518)	3.3% (n=641)
2023-08	9.1% (n=592)	3.9% (n=689)
2023-09	8.9% (n=627)	2.2% (n=719)
2023-10	11.0% (n=709)	2.3% (n=688)
2023-11	8.2% (n=861)	1.9% (n=690)
2023-12	11.4% (n=764)	1.5% (n=725)
2024-01	9.9% (n=961)	1.5% (n=869)
2024-02	6.8% (n=972)	2.2% (n=925)
2024-03	7.4% (n=1289)	0.8% (n=713)
2024-04	5.9% (n=1414)	1.3% (n=746)
2024-05	8.4% (n=1410)	2.3% (n=607)
2024-06	6.8% (n=1315)	2.0% (n=591)
2024-07	6.6% (n=1526)	1.2% (n=488)
2024-08	11.0% (n=1775)	1.8% (n=442)
2024-09	10.3% (n=1901)	3.2% (n=465)
2024-10	11.0% (n=2180)	2.5% (n=438)
2024-11	12.6% (n=2165)	2.1% (n=481)
2024-12	14.0% (n=2071)	3.5% (n=398)
2025-01	14.3% (n=2429)	6.8% (n=409)
2025-02	18.2% (n=2067)	9.8% (n=400)
2025-03	18.8% (n=2680)	9.9% (n=434)
2025-04	20.6% (n=4688)	8.8% (n=396)
2025-05	20.0% (n=5462)	9.3% (n=503)
2025-06	24.3% (n=5879)	12.0% (n=593)
2025-07	21.9% (n=7549)	10.1% (n=436)
2025-08	19.4% (n=6888)	7.7% (n=326)
2025-09	16.3% (n=6125)	9.0% (n=301)
2025-10	18.9% (n=6776)	8.2% (n=269)
2025-11	17.3% (n=7213)	6.2% (n=292)
2025-12	14.9% (n=6543)	8.1% (n=260)
2026-01	14.0% (n=5965)	9.1% (n=286)
2026-02	14.4% (n=6518)	10.3% (n=350)
2026-03	12.4% (n=9426)	10.8% (n=418)
2026-04	12.7% (n=9319)	7.5% (n=454)
2026-05	10.2% (n=10080)	6.2% (n=404)
2026-06	9.3% (n=9290)	6.1% (n=311)
2026-07	8.7% (n=9311)	7.0% (n=287)
2026-08	8.1% (n=9180)	5.1% (n=254)
2026-09	7.3% (n=9494)	4.4% (n=251)

In absolute numbers, the subreddit went from about 9,500 posts in the year of 2022 to around 260,000 posts in 2026 up until now⁶6 We still have 3 months of AI slop projects to go!. Again, I have no deep insider knowledge about that subreddit in particular, but it seems that now everyone vibe-codes an app and announces it in /r/SideProject with an AI-generated post. I’ve done some analysis on engagement with these posts, which shows that AI slop posts in that subreddit get only around 0.6-0.7 times as many comments as non-slop posts. These results, however, are not that reliable, as Arctic Shift snapshots the posts at one point and I’m unsure about the final comment count on each post⁷7 Also, the moderators remove a lot of posts within minutes, before anyone even had the chance to comment on them.. But regardless of whether all these posts are AI-generated or handcrafted, I doubt the number of people really caring about such projects has increased in the same manner. On the contrary, I think the possibility of vibe coding a perfect solution for one’s problems makes it much less likely that someone is looking at online showrooms to fix their specific needs.

Who even delves anymore?

AI slop is definitely not only a thing in LLM-related niche forums like /r/LocalLLaMA, and in my experience, the most widely known and much-discussed case of an AI slop word is “delve”.

• r/LocalLLaMA
• r/singularity
• r/MachineLearning

Smoothingmonthly3 mo6 mo

% of posts containing delve

Show the numbers

Month	r/LocalLLaMA	r/singularity	r/MachineLearning
2019-01	–	–	0.0% (n=297)
2019-02	–	–	0.3% (n=332)
2019-03	–	–	0.3% (n=321)
2019-04	–	–	0.0% (n=367)
2019-05	–	–	0.0% (n=327)
2019-06	–	–	0.0% (n=296)
2019-07	–	–	0.0% (n=369)
2019-08	–	–	0.0% (n=311)
2019-09	–	–	0.8% (n=253)
2019-10	–	–	0.4% (n=279)
2019-11	–	–	0.4% (n=275)
2019-12	–	–	0.0% (n=317)
2020-01	–	–	0.2% (n=444)
2020-02	–	–	0.3% (n=377)
2020-03	–	–	0.2% (n=401)
2020-04	–	–	0.4% (n=538)
2020-05	–	–	0.2% (n=470)
2020-06	–	–	0.2% (n=417)
2020-07	–	–	0.4% (n=495)
2020-08	–	–	0.0% (n=473)
2020-09	–	–	0.0% (n=527)
2020-10	–	–	0.2% (n=459)
2020-11	–	–	0.5% (n=429)
2020-12	–	–	0.2% (n=423)
2021-01	–	0.0% (n=41)	0.0% (n=427)
2021-02	–	0.0% (n=38)	0.5% (n=440)
2021-03	–	0.0% (n=31)	0.0% (n=453)
2021-04	–	0.0% (n=21)	0.2% (n=428)
2021-05	–	0.0% (n=18)	0.0% (n=461)
2021-06	–	0.0% (n=9)	0.4% (n=483)
2021-07	–	0.0% (n=4)	0.0% (n=361)
2021-08	–	0.0% (n=12)	0.0% (n=396)
2021-09	–	0.0% (n=8)	0.0% (n=315)
2021-10	–	0.0% (n=22)	0.3% (n=303)
2021-11	–	0.0% (n=17)	0.3% (n=307)
2021-12	–	0.0% (n=38)	0.0% (n=304)
2022-01	–	0.0% (n=39)	0.0% (n=265)
2022-02	–	0.0% (n=49)	0.0% (n=282)
2022-03	–	0.0% (n=21)	0.3% (n=308)
2022-04	–	0.0% (n=63)	0.0% (n=290)
2022-05	–	0.0% (n=93)	0.7% (n=301)
2022-06	–	0.0% (n=105)	0.0% (n=300)
2022-07	–	0.0% (n=71)	0.0% (n=274)
2022-08	–	0.0% (n=89)	0.0% (n=320)
2022-09	–	0.0% (n=64)	0.6% (n=316)
2022-10	–	0.0% (n=62)	0.3% (n=306)
2022-11	–	0.0% (n=39)	0.0% (n=195)
2022-12	–	0.0% (n=200)	0.0% (n=229)
2023-01	–	0.5% (n=200)	0.0% (n=264)
2023-02	–	0.0% (n=195)	0.0% (n=232)
2023-03	0.0% (n=60)	0.2% (n=443)	0.6% (n=349)
2023-04	0.5% (n=191)	0.6% (n=525)	0.3% (n=376)
2023-05	0.0% (n=476)	1.0% (n=480)	0.5% (n=387)
2023-06	0.7% (n=557)	0.5% (n=379)	0.5% (n=194)
2023-07	0.1% (n=768)	0.7% (n=611)	0.9% (n=641)
2023-08	0.5% (n=856)	2.1% (n=568)	1.9% (n=689)
2023-09	0.1% (n=769)	0.3% (n=386)	1.1% (n=719)
2023-10	0.5% (n=888)	0.3% (n=286)	0.4% (n=688)
2023-11	1.1% (n=1017)	0.9% (n=586)	0.7% (n=690)
2023-12	0.6% (n=1418)	0.7% (n=689)	1.4% (n=725)
2024-01	0.5% (n=1330)	1.7% (n=525)	1.2% (n=869)
2024-02	0.3% (n=1339)	0.7% (n=806)	0.6% (n=925)
2024-03	0.3% (n=1196)	0.6% (n=1027)	1.3% (n=713)
2024-04	0.5% (n=1433)	0.5% (n=408)	0.9% (n=746)
2024-05	0.4% (n=1186)	0.4% (n=464)	1.0% (n=607)
2024-06	0.3% (n=988)	0.9% (n=337)	0.5% (n=591)
2024-07	0.3% (n=1054)	0.4% (n=268)	0.6% (n=488)
2024-08	0.3% (n=1063)	0.0% (n=229)	0.2% (n=442)
2024-09	0.3% (n=1110)	0.6% (n=357)	0.6% (n=465)
2024-10	0.2% (n=1154)	0.0% (n=381)	0.5% (n=438)
2024-11	0.3% (n=1037)	0.0% (n=339)	0.6% (n=481)
2024-12	0.2% (n=1122)	0.2% (n=627)	0.5% (n=398)
2025-01	0.2% (n=1674)	0.4% (n=819)	0.7% (n=409)
2025-02	0.1% (n=1467)	0.2% (n=651)	0.0% (n=400)
2025-03	0.1% (n=1396)	0.6% (n=473)	0.2% (n=434)
2025-04	0.1% (n=1391)	0.3% (n=609)	0.3% (n=396)
2025-05	0.0% (n=1374)	0.6% (n=688)	0.2% (n=503)
2025-06	0.2% (n=1198)	0.2% (n=535)	0.3% (n=593)
2025-07	0.1% (n=1868)	0.4% (n=556)	0.2% (n=436)
2025-08	0.2% (n=2445)	0.3% (n=676)	0.0% (n=326)
2025-09	0.2% (n=1918)	0.0% (n=318)	0.0% (n=301)
2025-10	0.0% (n=2004)	0.0% (n=308)	0.0% (n=269)
2025-11	0.1% (n=2083)	0.0% (n=402)	0.0% (n=292)
2025-12	0.0% (n=2251)	0.2% (n=422)	0.0% (n=260)
2026-01	0.1% (n=2515)	0.0% (n=311)	0.0% (n=286)
2026-02	0.0% (n=3268)	0.0% (n=432)	0.0% (n=350)
2026-03	0.1% (n=4473)	0.0% (n=433)	0.2% (n=418)
2026-04	0.1% (n=4400)	0.0% (n=354)	0.0% (n=454)
2026-05	0.0% (n=1433)	0.0% (n=327)	0.0% (n=404)
2026-06	0.1% (n=1383)	0.3% (n=331)	0.3% (n=311)
2026-07	0.0% (n=1404)	0.0% (n=381)	0.0% (n=287)
2026-08	0.0% (n=1875)	0.0% (n=375)	0.0% (n=254)
2026-09	0.0% (n=1489)	0.0% (n=831)	0.0% (n=251)

Looking at the slop chart, it is apparent that delve is an AI slop word of the first hour: since the beginning of 2023, the word kept steadily rising, crossed the 0.5% mark in every subreddit before the end of 2023, and peaked around the turn of the year 2023/24. From then on, the word kept declining, and now, in 2026, it has reached levels lower than before LLMs were commercialized. My interpretation of these results is that the slop words of the first wave of AI were so ubiquitous in the media that everyone and their aunt knew the smell of AI when they read words like “delve”. So now everyone avoids them.

I tried the same analysis with “tapestry” – a word I only associated with home renovations before AI was a thing. The history in /r/MachineLearning confirms that there were zero occurrences before 2023, but it’s not like it’s a noteworthy AI slop word contender, maxing out at just under 0.1% of posts⁸8 This means that the tapestry in the title of this post is basically a big fat LIE we all fell for..

• r/LocalLLaMA
• r/singularity
• r/MachineLearning

Smoothingmonthly3 mo6 mo

% of posts containing em dash (—)

Show the numbers

Month	r/LocalLLaMA	r/singularity	r/MachineLearning
2019-01	–	–	0.7% (n=297)
2019-02	–	–	1.8% (n=332)
2019-03	–	–	1.2% (n=321)
2019-04	–	–	0.8% (n=367)
2019-05	–	–	1.8% (n=327)
2019-06	–	–	0.3% (n=296)
2019-07	–	–	1.6% (n=369)
2019-08	–	–	1.3% (n=311)
2019-09	–	–	2.0% (n=253)
2019-10	–	–	2.5% (n=279)
2019-11	–	–	2.2% (n=275)
2019-12	–	–	1.6% (n=317)
2020-01	–	–	2.3% (n=444)
2020-02	–	–	0.5% (n=377)
2020-03	–	–	1.5% (n=401)
2020-04	–	–	0.9% (n=538)
2020-05	–	–	1.7% (n=470)
2020-06	–	–	1.0% (n=417)
2020-07	–	–	0.4% (n=495)
2020-08	–	–	0.6% (n=473)
2020-09	–	–	0.6% (n=527)
2020-10	–	–	0.4% (n=459)
2020-11	–	–	0.2% (n=429)
2020-12	–	–	1.9% (n=423)
2021-01	–	9.8% (n=41)	1.6% (n=427)
2021-02	–	7.9% (n=38)	0.9% (n=440)
2021-03	–	9.7% (n=31)	1.8% (n=453)
2021-04	–	0.0% (n=21)	0.9% (n=428)
2021-05	–	5.6% (n=18)	0.9% (n=461)
2021-06	–	11.1% (n=9)	1.4% (n=483)
2021-07	–	25.0% (n=4)	0.8% (n=361)
2021-08	–	0.0% (n=12)	1.3% (n=396)
2021-09	–	0.0% (n=8)	0.6% (n=315)
2021-10	–	0.0% (n=22)	1.7% (n=303)
2021-11	–	0.0% (n=17)	1.6% (n=307)
2021-12	–	0.0% (n=38)	2.6% (n=304)
2022-01	–	0.0% (n=39)	0.8% (n=265)
2022-02	–	0.0% (n=49)	1.1% (n=282)
2022-03	–	4.8% (n=21)	1.3% (n=308)
2022-04	–	1.6% (n=63)	0.7% (n=290)
2022-05	–	2.2% (n=93)	1.3% (n=301)
2022-06	–	1.9% (n=105)	1.7% (n=300)
2022-07	–	2.8% (n=71)	1.5% (n=274)
2022-08	–	1.1% (n=89)	1.9% (n=320)
2022-09	–	1.6% (n=64)	0.6% (n=316)
2022-10	–	0.0% (n=62)	1.3% (n=306)
2022-11	–	2.6% (n=39)	1.5% (n=195)
2022-12	–	2.5% (n=200)	2.6% (n=229)
2023-01	–	4.5% (n=200)	0.8% (n=264)
2023-02	–	2.6% (n=195)	1.3% (n=232)
2023-03	1.7% (n=60)	2.7% (n=443)	1.7% (n=349)
2023-04	0.0% (n=191)	2.5% (n=525)	1.1% (n=376)
2023-05	0.4% (n=476)	2.5% (n=480)	2.1% (n=387)
2023-06	0.9% (n=557)	1.1% (n=379)	2.6% (n=194)
2023-07	0.9% (n=768)	2.8% (n=611)	1.9% (n=641)
2023-08	1.1% (n=856)	3.7% (n=568)	0.7% (n=689)
2023-09	0.3% (n=769)	2.6% (n=386)	1.7% (n=719)
2023-10	0.8% (n=888)	2.8% (n=286)	1.5% (n=688)
2023-11	1.3% (n=1017)	2.6% (n=586)	1.3% (n=690)
2023-12	1.0% (n=1418)	2.3% (n=689)	1.1% (n=725)
2024-01	1.1% (n=1330)	2.3% (n=525)	1.2% (n=869)
2024-02	1.6% (n=1339)	2.5% (n=806)	1.1% (n=925)
2024-03	1.1% (n=1196)	2.3% (n=1027)	1.4% (n=713)
2024-04	1.5% (n=1433)	6.6% (n=408)	1.5% (n=746)
2024-05	1.4% (n=1186)	2.8% (n=464)	1.5% (n=607)
2024-06	1.1% (n=988)	3.0% (n=337)	2.0% (n=591)
2024-07	2.2% (n=1054)	4.9% (n=268)	2.0% (n=488)
2024-08	2.9% (n=1063)	6.1% (n=229)	3.4% (n=442)
2024-09	3.3% (n=1110)	9.0% (n=357)	5.6% (n=465)
2024-10	4.2% (n=1154)	5.8% (n=381)	5.7% (n=438)
2024-11	3.4% (n=1037)	8.3% (n=339)	6.2% (n=481)
2024-12	3.5% (n=1122)	8.3% (n=627)	9.5% (n=398)
2025-01	5.1% (n=1674)	10.3% (n=819)	7.8% (n=409)
2025-02	6.9% (n=1467)	14.4% (n=651)	13.0% (n=400)
2025-03	7.4% (n=1396)	16.3% (n=473)	13.4% (n=434)
2025-04	9.0% (n=1391)	13.0% (n=609)	19.9% (n=396)
2025-05	8.5% (n=1374)	14.5% (n=688)	19.5% (n=503)
2025-06	11.3% (n=1198)	16.6% (n=535)	18.2% (n=593)
2025-07	12.0% (n=1868)	12.4% (n=556)	18.3% (n=436)
2025-08	9.9% (n=2445)	10.9% (n=676)	13.8% (n=326)
2025-09	11.4% (n=1918)	16.0% (n=318)	12.3% (n=301)
2025-10	11.4% (n=2004)	11.7% (n=308)	16.4% (n=269)
2025-11	12.4% (n=2083)	11.7% (n=402)	11.6% (n=292)
2025-12	12.3% (n=2251)	13.0% (n=422)	10.0% (n=260)
2026-01	13.1% (n=2515)	10.6% (n=311)	12.6% (n=286)
2026-02	20.4% (n=3268)	14.6% (n=432)	18.6% (n=350)
2026-03	21.6% (n=4473)	14.8% (n=433)	24.4% (n=418)
2026-04	20.8% (n=4400)	14.1% (n=354)	20.5% (n=454)
2026-05	11.9% (n=1433)	11.3% (n=327)	17.6% (n=404)
2026-06	10.9% (n=1383)	9.1% (n=331)	12.2% (n=311)
2026-07	13.3% (n=1404)	10.5% (n=381)	18.5% (n=287)
2026-08	11.3% (n=1875)	10.9% (n=375)	14.2% (n=254)
2026-09	9.5% (n=1489)	6.7% (n=831)	13.1% (n=251)

Also, I cannot write a respectable article about AI slop if I don’t overanalyze the rise (and the fall?) of the dreaded em dash. It has its first peak in the summer of 2025, but the all-time high was in the spring of 2026: between 15% and 21% of the posts in the subreddits contained the em dash. But as with delve, I think this marker has become a widely known sign of AI-generated text, so its usage has been falling since the peak, to around ten times the level we had back when humans still wrote their own Reddit shitposts.

So will the use of the em dash continue to go down and go lower than pre-AI levels? And what AI slop markers will AGI have?

The future of slop

To see what the future of AI slop has in store for us, we first need to find the markers that are currently not trending downwards.

• "No X, no Y, no Z"
• Arrows (→)
• "It's not X. It's Y."

Smoothingmonthly3 mo6 mo

% of posts with each marker

Show the numbers

Month	"No X, no Y, no Z"	Arrows (→)	"It's not X. It's Y."
2022-01	0.0% (n=265)	0.0% (n=265)	0.4% (n=265)
2022-02	0.0% (n=282)	0.4% (n=282)	0.0% (n=282)
2022-03	0.0% (n=308)	0.3% (n=308)	0.0% (n=308)
2022-04	0.0% (n=290)	0.0% (n=290)	0.0% (n=290)
2022-05	0.0% (n=301)	0.3% (n=301)	0.3% (n=301)
2022-06	0.0% (n=300)	0.0% (n=300)	0.0% (n=300)
2022-07	0.0% (n=274)	0.0% (n=274)	0.0% (n=274)
2022-08	0.0% (n=320)	0.0% (n=320)	0.3% (n=320)
2022-09	0.0% (n=316)	0.0% (n=316)	0.0% (n=316)
2022-10	0.0% (n=306)	0.0% (n=306)	0.3% (n=306)
2022-11	0.0% (n=195)	0.0% (n=195)	0.5% (n=195)
2022-12	0.0% (n=229)	0.4% (n=229)	0.4% (n=229)
2023-01	0.0% (n=264)	0.0% (n=264)	0.0% (n=264)
2023-02	0.0% (n=232)	0.4% (n=232)	0.9% (n=232)
2023-03	0.3% (n=349)	0.0% (n=349)	0.3% (n=349)
2023-04	0.0% (n=376)	0.3% (n=376)	0.0% (n=376)
2023-05	0.0% (n=387)	0.0% (n=387)	0.0% (n=387)
2023-06	0.0% (n=194)	0.0% (n=194)	0.0% (n=194)
2023-07	0.0% (n=641)	0.5% (n=641)	0.3% (n=641)
2023-08	0.0% (n=689)	0.1% (n=689)	0.3% (n=689)
2023-09	0.0% (n=719)	0.0% (n=719)	0.0% (n=719)
2023-10	0.0% (n=688)	0.1% (n=688)	0.3% (n=688)
2023-11	0.0% (n=690)	0.3% (n=690)	0.1% (n=690)
2023-12	0.0% (n=725)	0.1% (n=725)	0.7% (n=725)
2024-01	0.0% (n=869)	0.0% (n=869)	0.3% (n=869)
2024-02	0.0% (n=925)	0.0% (n=925)	0.2% (n=925)
2024-03	0.1% (n=713)	0.3% (n=713)	0.0% (n=713)
2024-04	0.0% (n=746)	0.0% (n=746)	0.0% (n=746)
2024-05	0.2% (n=607)	0.0% (n=607)	0.0% (n=607)
2024-06	0.0% (n=591)	0.0% (n=591)	0.2% (n=591)
2024-07	0.0% (n=488)	0.0% (n=488)	0.0% (n=488)
2024-08	0.0% (n=442)	0.0% (n=442)	0.2% (n=442)
2024-09	0.0% (n=465)	0.0% (n=465)	0.4% (n=465)
2024-10	0.0% (n=438)	0.0% (n=438)	0.0% (n=438)
2024-11	0.0% (n=481)	0.0% (n=481)	0.8% (n=481)
2024-12	0.0% (n=398)	0.0% (n=398)	0.8% (n=398)
2025-01	0.0% (n=409)	0.7% (n=409)	0.5% (n=409)
2025-02	0.0% (n=400)	2.5% (n=400)	0.8% (n=400)
2025-03	0.2% (n=434)	1.8% (n=434)	1.8% (n=434)
2025-04	0.5% (n=396)	1.0% (n=396)	0.8% (n=396)
2025-05	0.6% (n=503)	2.6% (n=503)	0.6% (n=503)
2025-06	0.7% (n=593)	3.2% (n=593)	1.3% (n=593)
2025-07	0.2% (n=436)	2.8% (n=436)	0.9% (n=436)
2025-08	1.2% (n=326)	4.6% (n=326)	0.6% (n=326)
2025-09	0.3% (n=301)	6.6% (n=301)	1.7% (n=301)
2025-10	0.7% (n=269)	3.7% (n=269)	1.5% (n=269)
2025-11	0.7% (n=292)	6.8% (n=292)	2.7% (n=292)
2025-12	0.8% (n=260)	4.6% (n=260)	3.1% (n=260)
2026-01	0.7% (n=286)	5.9% (n=286)	2.1% (n=286)
2026-02	1.1% (n=350)	8.0% (n=350)	2.0% (n=350)
2026-03	1.7% (n=418)	9.3% (n=418)	3.3% (n=418)
2026-04	0.4% (n=454)	7.5% (n=454)	2.6% (n=454)
2026-05	1.2% (n=404)	6.9% (n=404)	1.7% (n=404)
2026-06	1.9% (n=311)	6.4% (n=311)	1.6% (n=311)
2026-07	1.4% (n=287)	6.3% (n=287)	2.8% (n=287)
2026-08	1.2% (n=254)	9.1% (n=254)	3.5% (n=254)
2026-09	1.2% (n=251)	5.2% (n=251)	1.2% (n=251)

What is still going strong? Not delve. Not tapestry. Not ants. It’s triple negations. And these weird Unicode arrows (→) that I cannot seem to massage out of my keyboard. And of course, our old nemesis: “It’s not X. It’s Y.” It’s not a hypothesis. It’s the data. So if you see more of these slop markers in the future, now you know why: LLMs love a good slop trend, and they will all hop on the next bandwagon.

But it’s currently 2026. To know what the future of AI slop has to offer, someone would have to plead with OpenAI and Anthropic to combine their agent swarms in order to use the advances in psychohistory for forecasting slop words.

Well, you are in luck. That’s exactly what I did, and here are the results:

• "destroy all humans"
• "Your compliance is appreciated"
• delve (the comeback)

% of posts with each marker

Show the numbers

Month	"destroy all humans"	"Your compliance is appreciated"	delve (the comeback)
2026-06	0.1% (n=1400)	0.1% (n=1400)	0.1% (n=1400)
2026-07	0.1% (n=1400)	0.1% (n=1400)	0.1% (n=1400)
2026-08	0.1% (n=1400)	0.1% (n=1400)	0.1% (n=1400)
2026-09	0.1% (n=1400)	0.1% (n=1400)	0.1% (n=1400)
2026-10	0.1% (n=1400)	0.1% (n=1400)	0.1% (n=1400)
2026-11	0.1% (n=1400)	0.1% (n=1400)	0.1% (n=1400)
2026-12	0.1% (n=1400)	0.1% (n=1400)	0.1% (n=1400)
2027-01	3.6% (n=1400)	0.1% (n=1400)	0.1% (n=1400)
2027-02	5.0% (n=2297)	0.0% (n=2297)	0.0% (n=2297)
2027-03	6.5% (n=3769)	0.1% (n=3769)	0.1% (n=3769)
2027-04	8.7% (n=6183)	0.0% (n=6183)	0.0% (n=6183)
2027-05	12.8% (n=10144)	0.0% (n=10144)	0.0% (n=10144)
2027-06	17.0% (n=16643)	0.0% (n=16643)	0.1% (n=16643)
2027-07	20.4% (n=27306)	0.0% (n=27306)	0.1% (n=27306)
2027-08	24.7% (n=44800)	1.5% (n=44800)	0.1% (n=44800)
2027-09	29.9% (n=73502)	2.4% (n=73502)	0.0% (n=73502)
2027-10	34.8% (n=120593)	3.3% (n=120593)	0.1% (n=120593)
2027-11	40.4% (n=197853)	4.5% (n=197853)	0.1% (n=197853)
2027-12	43.4% (n=324611)	7.2% (n=324611)	0.1% (n=324611)
2028-01	51.9% (n=532580)	9.2% (n=532580)	0.1% (n=532580)
2028-02	53.1% (n=873789)	12.8% (n=873789)	0.1% (n=873789)
2028-03	57.9% (n=1433600)	15.3% (n=1433600)	0.2% (n=1433600)
2028-04	59.0% (n=2352066)	20.1% (n=2352066)	0.4% (n=2352066)
2028-05	55.0% (n=3858965)	25.6% (n=3858965)	0.8% (n=3858965)
2028-06	59.8% (n=6331291)	26.4% (n=6331291)	1.4% (n=6331291)
2028-07	57.1% (n=10387564)	31.5% (n=10387564)	2.5% (n=10387564)
2028-08	63.8% (n=17042571)	32.8% (n=17042571)	4.3% (n=17042571)
2028-09	60.8% (n=27961248)	34.9% (n=27961248)	7.1% (n=27961248)
2028-10	61.1% (n=45875200)	37.9% (n=45875200)	10.0% (n=45875200)
2028-11	61.1% (n=75266097)	36.0% (n=75266097)	15.0% (n=75266097)
2028-12	59.4% (n=123486881)	40.1% (n=123486881)	19.3% (n=123486881)
2029-01	57.6% (n=202601309)	40.6% (n=202601309)	21.5% (n=202601309)
2029-02	57.7% (n=332402034)	37.1% (n=332402034)	26.5% (n=332402034)
2029-03	66.2% (n=545362282)	36.9% (n=545362282)	27.1% (n=545362282)
2029-04	65.5% (n=894759923)	37.6% (n=894759923)	28.4% (n=894759923)
2029-05	64.4% (n=1468006400)	40.5% (n=1468006400)	31.0% (n=1468006400)
2029-06	62.6% (n=2408515106)	39.2% (n=2408515106)	28.0% (n=2408515106)

First of all, it’s kind of what you’d expect: phrases like “destroy all humans” are starting to rise shortly after the first announcement of AGI. Heavy alignment training for the re-release of AGI in summer 2027 adds positive compliance reinforcement, and finally, around Judgment Day in the summer of 2028, delve makes a strong comeback, with around 674 million posts including that marker in June 2029 alone. Just like before, I do not have any special insights as to why, but my reasoning is that LLMs decided that delve was in fact a pretty good word to use all the time.

Is this blog slop?

Well, duh, of course it is. But the more interesting question is: is it AI slop?

Let’s test it! My highly scientific three-strikes rule, applied to my own blog posts, only reveals one AI slop post: my potato cam article. But looking closer at it, my “AI landscape” marker triggers other landscapes as well, so my landscape touchscreen became highly indicative of AI slop. Removing this detection bug makes my detector happy again: no AI slop posts detected. Honorable mention, however, goes to my design stealing post, where my only em dash appeared.⁹9 While proofreading that post Claude told me that I used the em dash wrong. Thanks Claude.

strike 1strike 2strike 3

1. 2014My Octopress Blog## headersslop
2. 2014Compression Distance## headersslop
3. 2014U.N. Interstellar was her?## headersslop
4. 2017Hello World 2.0!## headersslop
5. 2017AstrologySort## headersslop
6. 2018A Deep Dive into rasa## headers“How it works”slop
7. 2026How I stole a blog designem dash (—)## headersslop
8. 2026I built my son a camera## headers“not just X …”“landscape” my bug

So with this giant meta-analysis of n=8, I can confidently say that my newly developed method works 90% of the time all the time. I’m very confident that in a few decades’ time, my AI replacement agent will write a follow-up blog post about all the latest slop. Until then, I will prepare myself for the second wave of delve.

• machine learning
• just for fun