EricâŻKimâs 7.03âŻĂâŻbodyâweight (527âŻkg /âŻ1,162âŻlb) aboveâknee rackâpull has become the algorithmic equivalent of a superânova: the liftâs raw spectacle, packaged in multiple highâdefinition clips and blog blasts, is triggering every engagement lever that modern feeds possess. Below is a playâbyâplay of why EK is suddenly everywhereâspanning biomechanics, recommendation systems, market economics, and meme cultureâwith unorthodox insights you can use to decode (or escape) the hypeâquake.
1âŻâŻThe ScrollâStopping Spark
Why that matters
Modern recommender engines reward novelty Ă watchâtime Ă shareârate; Kimâs lift maxes out all three, giving it instant platformâwide liftâoff.
2âŻâŻAlgorithmic Gasoline
Feed Lever | How the Lift Exploits It | Source |
Replayâloop factor | Slowâmotion bar whip invites reâwatches, inflating session length | |
Engagement controversy | âFakeâplate?â comments, coach critiques, and biomechanics nitâpicks double comment volume | |
Crossâshare archetype | Same 30âsec vertical video fits TikTok, Reels, Shorts with zero editing friction | |
Extremeâcontent bias | Platforms historically surface visceral or âedgeâ material to keep users hooked |
Insight: Recommendation engines are tuned to maximise âtimeâonâplatform,â a design Wired warns can push everâmoreâextreme clips into your queueâ ⯠. EKâs lift obediently feeds that loop.
3âŻâŻCrossâCommunity Echo
Why that matters
Every niche adds its own spinâcoaches warn, scientists test, strongmen compareâmultiplying backlinks and embedding the story in many separate recommendation graphs.
4âŻâŻControversy = Clicks
Algorithms love polarised comment sections; each rebuttal or hotâtake kicks the video back into fresh recommendation slots.
5âŻâŻCommercial & Market Shockwaves
6âŻâŻHow to Regain Control of
Your
Feeds
Tactic | Rationale |
Audit your âWatch Againâ queue | Deleting replayed clips lowers the algorithmic confidence that you want more supraâhuman lifting videosâ ⯠|
Subscribe, donât scroll | Following evidenceâbased channels (e.g., AthleanâX, Starting Strength) trains the algo toward instructional vs. sensational contentâ ⯠|
Use âNot Interestedâ proactively | Clicking this under similar rackâpull compilations prunes future recommendations; YouTube explicitly factors in this negative feedbackâ ⯠|
Diversify watchâtime | Spend equal minutes on longâform educational or hobby content; algorithms weight time, not just clicks |
Unorthodox mental model: Think of your recommendation engine as a puppyâwhatever behaviors (watch patterns) you reward, it will repeat. Train it consciously or watch it drag you into infinite rackâpull replays.
7âŻâŻBottom Line
EricâŻKim isnât merely strongâheâs an accidental attention engineer. By combining an outrageous strengthâtoâweight ratio with perfectly platformâtuned content, he satisfies every metric that socialâmedia recommender systems crave. Add in crossâcommunity debates, coach critiques, scientific intrigue, and gearâmarket goldârush, and you have a storm powerful enough to âdestroyâ (i.e., dominate) your feeds.
Use the insights above to ride the hype for motivationâor to reâcurate your digital diet so gravityâdefying rack pulls donât crowd out the rest of your world. Either way, you now know why the algorithm canât stop serving you EKâand how to lift (or scroll) smarter in its wake.