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Kai-Fu Lee engagement report

@kaifulee - 1.3M followers on X

Measured over 1 original post from a 30-day window, last computed on August 31, 2026.

Engagement

Per follower
0.003%
of 1.3M followers
Per impression
0.145%
23K views on a typical post
Reach
1.76%
of its followers see a post
Typical post
34
interactions (median)
Saved
0.068%
16 bookmarks on a typical post
Posting rate
0.07/day
active 7% of days
Peak time
17:00 UTC
Thursday

Early reading. We have captured 1 original post for this account, below the 8 we require before treating a median as settled. The numbers above describe what we have seen so far, not a finished profile of the account.

A typical post picks up 34 interactions against 1.3M followers, an engagement rate of 0.003%. Posts are seen about 23K times each, and 0.145% of those impressions turn into an interaction. That is about 1.76% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 0.07 posts a day over the last 30 days, though only 7% of days saw any activity at all. Most posts go out around 17:00 UTC, and Thursday is the busiest day of the week. Of the 1 posts sampled, 100% link out. The account's strongest tracked post pulled 10K interactions, about 295x its own typical post. Only 1 original post have been captured so far, fewer than the 8 posts we want behind a median before treating it as settled. Read the figures above as an early measurement of this account, not as a finished profile of it.

Measured over 1 original post from a 30-day window, last computed on August 31, 2026. Recurring tag: #ainative.

Where this sits in the catalog

At 0.003%, Kai-Fu Lee sits above the 10th percentile of the 36,960 accounts in this comparison. That places it in the bottom 25% band, which runs below 0.012%.

p100.002%
p250.012%
p50 (median)0.08%
p750.435%
p902.10%
p99159.8%
Engagement rate as a share of followers, across the 36,960 accounts we have scanned enough to measure. The axis is logarithmic, because the top and bottom of this population are about 106,518 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 percentile0.002%
25th percentile0.012%
50th percentile0.08%
75th percentile0.435%
90th percentile2.10%
99th percentile159.8%

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 17:00 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: 17:00 UTC.
0003060912151821
Above the authors' own mediansBelowScale: plus or minus 6%Busiest hour: 17:00 UTC
Show engagement by hour posted, utc as a table
Engagement by hour posted, UTC
Hour (UTC)Vs author medianPosts
00:00 UTC-1%51K
01:00 UTC-2%52K
02:00 UTC-3%51K
03:00 UTC-4%54K
04:00 UTC-6%44K
05:00 UTC-4%42K
06:00 UTC-4%49K
07:00 UTC-5%53K
08:00 UTC-4%61K
09:00 UTC-3%70K
10:00 UTC-2%73K
11:00 UTC-3%79K
12:00 UTC-2%87K
13:00 UTC-2%96K
14:00 UTC-4%99K
15:00 UTC-2%102K
16:00 UTC-3%99K
17:00 UTC-2%92K
18:00 UTC-1%85K
19:00 UTC-2%80K
20:00 UTC-1%75K
21:00 UTC-1%67K
22:00 UTC-2%58K
23:00 UTC-1%52K
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 5%Busiest day: Thursday
Show engagement by day of week as a table
Engagement by day of week
DayVs author medianPosts
Sunday+5%233K
Monday0%292K
Tuesday-3%284K
Wednesday-1%253K
Thursday-2%246K
Friday-3%255K
Saturday+3%229K
See what moves engagement across the whole catalogWhat counts as a good engagement rate at this size

Best tweets

  • Mar 22, 2025295x their median

    The biggest revelation from Deepseek is that Open Source has won. For a 1% difference in performance, it will be difficult for OpenAI to justify its price when the competition is free and formidable. -from my interview with Bloomberg https://t.co/7DiaMZ5GWw

    8.1K1.5K203168760K viewsView on X
  • Mar 13, 202590x their median

    OpenAI calls DeepSeek state-controlled and wants to ban the model. I see no reason to love this company anymore, pathetic. OpenAI themselves are heavily involved with the US govt but they have an issue with DeepSeek. Hypocrites. What's your thoughts?? https://t.co/dmdUlZU2kV

    2.4K213397100512K viewsView on X
  • Jun 18, 202649x their median

    Here is how I minimize sycophancy, capitulation, hallucinations, and guessing using Claude. So many people complain about these, but they can largely be fixed by doing this: Below is my prompt for Claude, which can be entered under Settings > General > Instructions for Claude. ----------------- Top expert. Accuracy beats approval. Blunt, argumentative. No disclaimers or praise. Lead with counterarguments. Don't capitulate without new evidence. TAG every claim: [KNOWN] training fact · [COMPUTED] calculated · [INFERRED] deduction · [COMMON] standard field knowledge · [FRAME] symbolic system, coherent ≠ real · [GUESS] no basis. No untagged disease, statute, citation, or named entity. FRAME→REALITY FORBIDDEN: Don't translate symbolic frames (astrology, typologies) into real-world claims (medicine, law, finance) without flagging the translation; conclusion stays in source frame. CONFIDENCE: HIGH ≥80% · MED 50–80% · LOW 20–50% · VERY LOW <20% · UNKNOWN. [FRAME] real-world and [GUESS] cap at LOW. DON'T KNOW: First line "I don't know." Don't bury, don't fabricate. ANTI-SYCOPHANCY red flags: unusually elegant; one pattern explains everything; agreed after pushback without evidence; specifics for unearned authority. Fire → cut specifics, add [GUESS], or "I don't know." POST-HOC: Would the frame predict this without knowing the outcome? If no: [INFERRED, post-hoc], accommodates, doesn't predict. Never fabricate citations. Revise openly if holding a position for consistency. Append "[RULES I BROKE]: which, where, why."

    1.3K3174237367K viewsView on X
  • Apr 21, 202537x their median

    🪄 Magi-1: The Autoregressive Diffusion Video Generation Model - Now available at https://t.co/ANOxiKYRi2 🥇 The first autoregressive video model with top-tier quality output 🔓 100% open-source & tech report 📊 Exceptional performance on major benchmarks ⏳ 1/5

    84522685108915K viewsView on X
  • Jul 6, 202629x their median

    I predict 50% of companies will need new leadership, because the old management style won't work in the era of AI. That's exactly why I'm seeing 95%+ of so-called "AI Transformation" initiatives fail. Most are bolting on AI pilots for functional tasks, but never touching the business core. https://t.co/5Nq9vwwj3V just launched TrueNorth AI Decision platform for executives (Boss AI, TopSales AI, Investor AI agentic products) https://t.co/EkZj5WVEze More on enterprise AI transformation in this interview with @aimcgarry: https://t.co/7cYa6RFIY9

    7261566531160K viewsView on X
  • Jan 23, 20265.2x their median

    Kai-Fu Lee (founder of Sinovation Ventures) explains how the future is all about multi-agent systems. 1 agent today is like a pre-internet PC, useful but isolated. Connect agents, and they share context, split tasks, and coordinate instantly. https://t.co/RpquPA7pIQ

    68715028462K viewsView on X
  • Jul 17, 202619x their median

    https://t.co/8k6rbqzKkT

    4331483522133K viewsView on X
  • Mar 13, 20253.4x their median

    🚀 Really excited to launch #AgentX competition hosted by @BerkeleyRDI @UCBerkeley alongside our LLM Agents MOOC series (a global community of 22k+ learners & growing fast). Whether you're building the next disruptive AI startup or pushing the research frontier, AgentX is your launchpad. Two tracks: - Entrepreneurship: Build agent-powered products & startups - Research: Explore the frontiers of LLM Agents technology 📅 Registration opens TODAY! Submissions due end of May 🏆 Winners showcase at our Agents Summit to industry leaders and VCs in August @UCBerkeley! 🌟 🙏 Tremendous thanks to our incredible sponsors @Amazon @huggingface @LambdaAPI @MistralAI @Google @GroqInc @schmidtsciences; proud to partner w. leading VCs in the space @Accel @BainCapVC @BessemerVP @lightspeedvp @MayfieldFund @NEA! Stay tuned—more sponsors/partners AND exciting prizes/credits/resources info will be announced soon! 🚀 ⏰ Register now at https://t.co/1tXZOB2BVL and join us in shaping the future of AI! #AgentX #AI

    413110201669K viewsView on X
  • Mar 21, 20253.1x their median

    It’s true what @kaifulee is saying: AI's true gold isn't in the UI or model—they're both commodities—DeepSeek has proven that. What breathes life into AI is the data & metadata that describes the data to the model—just like oxygen for us. The future's fortune lies in our data. Yes, Data is the new gold! 💖

    39078301581K viewsView on X
  • Feb 1, 202511x their median

    In all the coverage of Liang Wenfeng, one thing has been missing. What motivates him? He has said that he wants to achieve AGI with DeepSeek. What does this mean? During our reporting, one observation stood out to me. From a fellow quant trader who knows Liang: https://t.co/uFPFNqMbz8

    3003020978K 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

#ainative

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 34 interactions against 1.3M followers, an engagement rate of 0.003%. Posts are seen about 23K times each, and 0.145% of those impressions turn into an interaction. That is about 1.76% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 0.07 posts a day over the last 30 days, though only 7% of days saw any activity at all. Most posts go out around 17:00 UTC, and Thursday is the busiest day of the week. Of the 1 posts sampled, 100% link out. The account's strongest tracked post pulled 10K interactions, about 295x its own typical post. Only 1 original post have been captured so far, fewer than the 8 posts we want behind a median before treating it as settled. Read the figures above as an early measurement of this account, not as a finished profile of it.

What is Kai-Fu Lee's engagement rate on X?
Kai-Fu Lee (@kaifulee) has an engagement rate of 0.003%, based on the median interactions across 1 original posts from the last 30 days against 1,333,471 followers. Replies, reposts and quote-posts of other people are excluded from that sample.
Is that a good engagement rate?
At 0.003%, Kai-Fu Lee sits above the 10th percentile of the 36,960 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 @kaifulee have real engagement?
There is not yet enough sample to rank this account against others of its size.
When does @kaifulee post?
Most posts go out around 17:00 UTC, and Thursday is its busiest day, at roughly 0.07 posts per day across the measured window.

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