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Vinod Khosla engagement report

@vkhosla - 780K followers on X

Measured over 19 original posts from a 30-day window, last computed on September 29, 2026.

Engagement

Middle of its size range
Per follower
0.051%
of 780K followers
Per impression
0.418%
94K views on a typical post
Reach
12.1%
of its followers see a post
Typical post
392
interactions (median)
Saved
0.049%
46 bookmarks on a typical post
Posting rate
2.67/day
active 73% of days
Peak time
19:00 UTC
Thursday

A typical post picks up 392 interactions against 780K followers, an engagement rate of 0.051%. Measured over 19 original posts, its engagement rate beats 59% of 20,444 tracked accounts of a similar size, which puts it in the middle of its size range rather than at either end. Posts are seen about 94K times each, and 0.418% of those impressions turn into an interaction. That is about 12.0% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 2.7 posts a day over the last 30 days, with activity on roughly 73% of days. Most posts go out around 19:00 UTC, and Thursday is the busiest day of the week. Of the 19 posts sampled, 42% carry an image or video, 5% are part of a thread and 37% link out. The account's strongest tracked post pulled 10K interactions, about 27x its own typical post.

Measured over 19 original posts from a 30-day window, last computed on September 29, 2026. Recurring tag: #seahawks.

Compared with accounts its own size

Vinod Khosla's engagement rate beats 59% of the tracked X accounts closest to it in follower count (20,444 accounts, accounts of similar size (decile 10 of 10)). A percentile is spread evenly by construction, so 50 really is the middle of that group and 90 really is its top tenth.

On engagement per impression rather than per follower it beats 27% of the same group. When those two numbers disagree, the gap is about how far its posts travel rather than how people react to them.

Where this sits in the catalog

At 0.051%, Vinod Khosla sits above the 25th percentile of the 204,694 accounts in this comparison. That places it in the below the median band, which runs 0.024% to 0.143%.

p100.004%
p250.024%
p50 (median)0.143%
p750.677%
p902.55%
p9982.4%
Engagement rate as a share of followers, across the 204,694 accounts we have scanned enough to measure. The axis is logarithmic, because the top and bottom of this population are about 22,880 times apart and a linear axis would flatten everything below the median into a single point.
Show the percentile table
Engagement rate percentiles
PercentileEngagement rate
10th percentile0.004%
25th percentile0.024%
50th percentile0.143%
75th percentile0.677%
90th percentile2.55%
99th percentile82.4%

This ruler is the whole measured catalog, not a size-matched group: it shows where the raw rate falls across every account we can measure, all of which are large. For a like-for-like comparison, read the size-band percentile above instead. See how the bands are built

Posting timing

This account posts most often around 19:00 UTC, and Thursday is its busiest day of the week. The bars below are the catalog-wide pattern, with this account's own busiest slot marked. They do not show how this account performs at each hour: we keep one aggregate per account, not one per hour, so that measurement does not exist in our data.

Engagement by hour posted, UTCTwenty-four bars, one per UTC hour. Each bar shows how posts published in that hour compare with their own authors' median engagement. Bars above the centre line ran higher than the median, bars below ran lower. A marker flags Busiest hour: 19:00 UTC.
0003060912151821
Above the authors' own mediansBelowScale: plus or minus 111%Busiest hour: 19:00 UTC
Show engagement by hour posted, utc as a table
Engagement by hour posted, UTC
Hour (UTC)Vs author medianPosts
00:00 UTC-1%89K
01:00 UTC-2%90K
02:00 UTC-3%88K
03:00 UTC-4%94K
04:00 UTC-5%76K
05:00 UTC-4%75K
06:00 UTC-5%86K
07:00 UTC-5%93K
08:00 UTC-4%108K
09:00 UTC-4%124K
10:00 UTC-3%129K
11:00 UTC-3%141K
12:00 UTC-3%154K
13:00 UTC-3%167K
14:00 UTC-4%173K
15:00 UTC-2%176K
16:00 UTC-3%171K
17:00 UTC-3%159K
18:00 UTC-2%149K
19:00 UTC-2%141K
20:00 UTC-1%131K
21:00 UTC0%116K
22:00 UTC-2%100K
23:00 UTC-1%90K
Engagement by day of weekSeven bars, one per weekday, Sunday first. Each bar shows how posts published on that day compare with their own authors' median engagement. Bars above the centre line ran higher than the median, bars below ran lower. A marker flags Busiest day: Thursday.
SunMonTueWedThuFriSat
Above the authors' own mediansBelowScale: plus or minus 111%Busiest day: Thursday
Show engagement by day of week as a table
Engagement by day of week
DayVs author medianPosts
Sunday+5%393K
Monday+1%483K
Tuesday-2%520K
Wednesday-3%472K
Thursday-2%430K
Friday-3%447K
Saturday+2%393K
See what moves engagement across the whole catalogWhat counts as a good engagement rate at this size

Formats this account uses

Its own posting mix on the left, and what each of those formats does across every account we track on the right. Only formats where the effect clears our publish test appear here, so an empty row is a format we could not measure rather than one that does nothing.

This account's posting mix compared with catalog-wide effects
FormatThis accountCatalog effect95% intervalAccounts behind it
Image or video42% of posts+111%+108% to +115%34K
Outbound link37% of posts-41%-42% to -40%32K
Typical length-+15%+14% to +16%32K
  • 42% of this account's sampled posts carry an image or video. Across the catalog, posts with an image or video run 111% above the same accounts' other posts.
  • 37% of its posts carry a link off X. Across the catalog, posts with an outbound link run 41% below the same accounts' other posts.
  • Its average post runs 320 characters, which falls in the over 280 characters band. Across the catalog, posts over 280 characters run 15% above the same accounts' other posts.

These are catalog-wide differences applied to this account's own posting mix, not a measurement of how each format performs for this account specifically. We keep one median per account, not one per format per account, so the second thing is not something this data can tell you.

Best tweets

  • Sep 10, 202627x their median

    I’m going to get a cardiac test before the next @Seahawks game. The team and 12s won it tonight. Hard to imagine a better roller coaster

    9.6K41038087383K viewsView on X
  • Sep 3, 202623x their median

    A legacy to preserve ... https://t.co/2wXpF1sBtS

    8.1K44423845384K viewsView on X
  • Sep 2, 20267.8x their median

    Excited for the @Seahawks home opener: https://t.co/eJSIArIHfZ

    2.8K119105987K viewsView on X
  • Aug 27, 20265.6x their median

    One of the coolest logo's @Seahawks and fan brand "12's Everywhere" https://t.co/MfcQPo50Rd

    2.0K851081294K viewsView on X
  • Sep 3, 20265.0x their median

    Our new Newf puppy named HAWK says "Win, Win, Win!" https://t.co/CKmNUpFjn9

    1.8K786115119K viewsView on X
  • Sep 2, 20263.0x their median

    Mike Macdonald says culture is basically a fancy word people overcomplicate “It’s a fancy word. We don’t say culture. What is it exactly? I don’t know. Then you start using a bunch of buzzwords and stuff and now you sound like an idiot.” “It’s just how you do everything. It’s just how you operate. Just how we roll. What’s important to us. John Harbaugh said, ‘Shared understanding of principles.’ I thought that was a pretty cool definition.” “What’s helped this year is tightening it down, coming into focus about who we want to be, how we want to operate, what’s important to us. Making it resonate with the guys more.” “Having a more effective message. And hopefully they’re buying in. I think they are. It feels like it. We’ve got great energy. To see everybody celebrate each other is just what it’s all about.”

    1.1K721618334K viewsView on X
  • Sep 27, 20261.9x their median

    When the call is overturned in less than 5 seconds after a challenge, the team should get their challenge back. That's just a terrible call, and the team should not be penalized for it.

    72017121147K viewsView on X
  • Sep 13, 20261.7x their median

    Alignment is not as hard to solve as many claim, but it is in the end an algorithmic problem which a lot of ML community mostly stopped working on. Formulation of alignment stated by three (or four) laws of robotics can take us very far, so we roughly know the objective. The tricky part is, how do we take gradient with respect to alignment? We have two algorithms right now at our disposal: pretraining and RL. Pretraining takes gradient with respect to next token prediction - that's def not the alignment objective. We could create RL environments that embody the alignment objective, but: - those are expensive to create, so often cheaper, hackable proxies are used in practice - RL as an objective needs successful and unsuccessful rollouts to happen to take the gradient step. We DO NOT want to harm any humans in the process of aligning our modes - this is a pretty big problem. Therefore there are two solutions forward for the alignment problem: - either we RL models in a simulated environments with simulated alignments and decreasing the likelihood of harming simulated humans, which will never be perfect - or we create a new algorithm that can teach our models to not harm humans without harming any humans in the process Science is the process how we solve the hardest problems ahead and that is one of them

    586344913110K viewsView on X
  • Aug 12, 20261.6x their median

    AI has created a 21st-century hyperabundance of potential new drugs and biological hypotheses, but we are still using 18th-century style animal testing. Instead, @Vivodyne is evaluating drugs in living human tissues at industrial scale. Excited to continue working with Vivodyne as they iterate on their 'Virtual Human' data center, where we can predict treatment response.

    5145551950K viewsView on X
  • Sep 13, 20261.6x their median

    Many great technologies have good and bad uses like nuclear and biotechnology. AI is no different in having great uses for the bottom three billion people on the planet and bad uses. Main difference no treaties are possible because unlike other technologies AI is not verifiable when used. Up to people like @DavidSacks to keep this danger front and center. Chinese originated Covid, despite treaties, caused 7million direct deaths and over 30 million including indirect deaths. And that is a verifiable tech we could detect when used.

    488427812105K viewsView on X

Ranked by total interactions across everything we have tracked for this account, which is a longer history than the 30-day window the rates above use. The multiple compares each post to this account's own median.

Recurring topics

#seahawks

The most frequent hashtags in the sampled posts. They describe what this account writes about; they are not a performance signal, and the catalog-wide breakdown on the hub shows how little hashtag count moves.

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Reading these numbers

A typical post picks up 392 interactions against 780K followers, an engagement rate of 0.051%. Measured over 19 original posts, its engagement rate beats 59% of 20,444 tracked accounts of a similar size, which puts it in the middle of its size range rather than at either end. Posts are seen about 94K times each, and 0.418% of those impressions turn into an interaction. That is about 12.0% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 2.7 posts a day over the last 30 days, with activity on roughly 73% of days. Most posts go out around 19:00 UTC, and Thursday is the busiest day of the week. Of the 19 posts sampled, 42% carry an image or video, 5% are part of a thread and 37% link out. The account's strongest tracked post pulled 10K interactions, about 27x its own typical post.

What is Vinod Khosla's engagement rate on X?
Vinod Khosla (@vkhosla) has an engagement rate of 0.051%, based on the median interactions across 19 original posts from the last 30 days against 780,017 followers. Replies, reposts and quote-posts of other people are excluded from that sample.
Is that a good engagement rate?
At 0.051%, Vinod Khosla sits above the 25th percentile of the 204,694 accounts in this comparison. Those comparison accounts are all large ones, because our scanning cadence is weighted towards big accounts, so this is a ranking among peers of similar scale rather than a ranking across X.
Does @vkhosla have real engagement?
Its engagement rate beats 59% of the tracked X accounts closest to it in follower count (20,444 accounts), which puts it in the middle of its size range group. Ranking inside a size band matters because engagement rate falls as accounts grow, so a raw rate would mostly re-measure the follower count. It is a starting point for a look at follower quality, not a verdict on it.
When does @vkhosla post?
Most posts go out around 19:00 UTC, and Thursday is its busiest day, at roughly 2.67 posts per day across the measured window.

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