Sequoia Capital engagement report
@sequoia - 815K followers on X
Measured over 6 original posts from a 30-day window, last computed on August 31, 2026.
Engagement
Early reading. We have captured 6 original posts 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 269 interactions against 815K followers, an engagement rate of 0.033%. Posts are seen about 51K times each, and 0.531% of those impressions turn into an interaction. That is about 6.22% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 2 posts a day over the last 30 days, though only 43% of days saw any activity at all. Most posts go out around 17:00 UTC, and Wednesday is the busiest day of the week. Of the 6 posts sampled, 83% carry an image or video and 17% are part of a thread. The account's strongest tracked post pulled 740 interactions, about 2.8x its own typical post. Only 6 original posts 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 6 original posts from a 30-day window, last computed on August 31, 2026.
Where this sits in the catalog
At 0.033%, Sequoia Capital sits above the 25th percentile of the 36,654 accounts in this comparison. That places it in the below the median band, which runs 0.012% to 0.08%.
Show the percentile table
| Percentile | Engagement rate |
|---|---|
| 10th percentile | 0.002% |
| 25th percentile | 0.012% |
| 50th percentile | 0.08% |
| 75th percentile | 0.433% |
| 90th percentile | 2.10% |
| 99th percentile | 160.5% |
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 Wednesday 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.
Show engagement by hour posted, utc as a table
| Hour (UTC) | Vs author median | Posts |
|---|---|---|
| 00:00 UTC | -1% | 51K |
| 01:00 UTC | -2% | 52K |
| 02:00 UTC | -3% | 50K |
| 03:00 UTC | -4% | 53K |
| 04:00 UTC | -6% | 43K |
| 05:00 UTC | -4% | 42K |
| 06:00 UTC | -4% | 48K |
| 07:00 UTC | -5% | 52K |
| 08:00 UTC | -4% | 61K |
| 09:00 UTC | -3% | 70K |
| 10:00 UTC | -2% | 72K |
| 11:00 UTC | -3% | 78K |
| 12:00 UTC | -2% | 86K |
| 13:00 UTC | -2% | 94K |
| 14:00 UTC | -4% | 97K |
| 15:00 UTC | -2% | 101K |
| 16:00 UTC | -3% | 98K |
| 17:00 UTC | -2% | 91K |
| 18:00 UTC | -1% | 85K |
| 19:00 UTC | -1% | 80K |
| 20:00 UTC | -1% | 74K |
| 21:00 UTC | -1% | 66K |
| 22:00 UTC | -1% | 57K |
| 23:00 UTC | -2% | 52K |
Show engagement by day of week as a table
| Day | Vs author median | Posts |
|---|---|---|
| Sunday | +4% | 231K |
| Monday | 0% | 288K |
| Tuesday | -2% | 278K |
| Wednesday | -1% | 251K |
| Thursday | -1% | 245K |
| Friday | -3% | 253K |
| Saturday | +3% | 227K |
Best tweets
- Jun 12, 20262.8x their median
Ad Astra @elonmusk and @SpaceX 🫡🚀 https://t.co/HJReXx3sS2
- Aug 11, 20261.8x their median
OWN YOUR INTELLIGENCE Last year, building on open-weight models was primarily a cost rationalization exercise. Slightly worse performance for a much cheaper price. Now, it is increasingly an existential and strategic topic for our portfolio. Intelligence is the product. Companies want to shape it and own it and let it compound within their own walls. Not your weights, not your product. Now, with frontier open-weight models and fantastic tooling/infrastructure, owning your intelligence at the frontier is finally becoming possible. The result: every application company we work with is embarking on the journey of doing their own research on post-training, evals, harnesses, etc. The hottest neolabs may just be @Harvey, @FactoryAI, @Ramp, etc. The list goes on. We held a summit @sequoia to convene our portfolio on this topic, together with @gabepereyra (@Harvey) on building Harvey Labs, @lqiao (@FireworksAI_HQ) on post-training, @hwchase17 (@LangChain) on harnesses + evals, @BrendanFoody (@mercor) on RL environments and synthetic data, @QuantumArjun (@trajectorylabs) on online continual learning. Opening talk below; rest to come this week! 00:00 What is sovereign AI (and what it isn't) 01:24 Centralized vs. decentralized intelligence 02:54 Four reasons companies own their models: cost, speed, performance, destiny 04:22 "Not your weights, not your product" 05:32 The application companies are the newest neo labs 07:05 Step 1: Deciding what to own vs. rent 09:51 Step 2: Build the team (and don't shoehorn your platform team) 11:17 Step 3: Legibility – why your research has to be visible 12:33 Step 4: The technical roadmap 13:56 The stack: production vs. development 15:16 Opening Pandora's box – base models, harnesses, context
- Aug 12, 2026
Preview has raised $12M in funding to date, led by @sequoia with participation from @thegp and @farooqib. AI video is now part of how real productions get made. What professional work requires hasn't changed: scripts, shot lists, versions, reviews, deliveries, and a clear record of how every frame was made. Preview is the professional platform where AI video production runs. Producers, directors, and artists planning, generating, reviewing, and delivering together, from first idea to editor handover. Studios already run real work on Preview: commercial productions for Fortune 100 brands, and some of the first hybrid films with Oscar-winning talent, arriving on streaming platforms later this year. 3,000 studios are on the waitlist. We're letting the next ones in now.
- Aug 13, 2026
Intelligence and Experience are orthogonal vectors Terence Tao is perhaps the world’s smartest person, but drop him into an accounting firm or onto a construction site and on day one he’s not going to be very productive @trajectorylabs calls this The Experience Gap, and they have a way to close it @QuantumArjun explained how at our Sovereign AI event: 00:00 Introduction 00:12 Building the platform for continual learning 01:33 The experience gap: models have IQ but no tenure 02:52 Traceability → model spec → better models and harnesses 05:27 Four wishes for the agent ecosystem 06:34 Wish 1: Trace the whole tree — and capture the corrections 08:03 Wish 2: Evals from real traffic, graded in the real harness 09:26 Wish 3: Let the agents cook, and make tool responses informative 10:34 Wish 4: Get comfortable on open weights, experiment with routers 11:51 Why owning your intelligence shouldn't be consulted away 13:15 Demo: import a benchmark, train a model, deploy it 14:28 Q&A: What's the trainable object — weights, harness, or context? 16:08 Q&A: Continual learning without training on customer data 17:13 Q&A: Episodic memory and the hierarchy of feedback 19:37 Q&A: Where continual learning matters most
- Aug 27, 2026
Yay, in TIME100 AI w/ @Azaliamirh ! With AlphaChip, we started the field of AI for Chip Design. Last year, we founded Ricursive to take on all of chip design, from model to GDS (the final input to the fab). How we got here: (1/n) https://t.co/YJ7OQQbRcX
- Aug 24, 2026
"Is it Bitter Lesson-pilled?" — the most used, and most misused, phrase in AI right now. @RichardSSutton sums it up on the latest Training Data with @kjaved_ , @sonyatweetybird, @Alfred_Lin "Don't be distracted by human knowledge, as AI traditionally has been many times. Instead, focus on learning methods that will scale with computation, like search and like learning."
- Aug 5, 2026
Thanks to @Kantrowitz for having me on the @BigTechPod last Monday. We discussed the strategies of each of the big tech companies, and why I think AI is turning into the greatest strategy game in history, a la StarCraft or Azad.
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.
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Reading these numbers
A typical post picks up 269 interactions against 815K followers, an engagement rate of 0.033%. Posts are seen about 51K times each, and 0.531% of those impressions turn into an interaction. That is about 6.22% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 2 posts a day over the last 30 days, though only 43% of days saw any activity at all. Most posts go out around 17:00 UTC, and Wednesday is the busiest day of the week. Of the 6 posts sampled, 83% carry an image or video and 17% are part of a thread. The account's strongest tracked post pulled 740 interactions, about 2.8x its own typical post. Only 6 original posts 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 Sequoia Capital's engagement rate on X?
- Sequoia Capital (@sequoia) has an engagement rate of 0.033%, based on the median interactions across 6 original posts from the last 30 days against 814,953 followers. Replies, reposts and quote-posts of other people are excluded from that sample.
- Is that a good engagement rate?
- At 0.033%, Sequoia Capital sits above the 25th percentile of the 36,654 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 @sequoia have real engagement?
- There is not yet enough sample to rank this account against others of its size.
- When does @sequoia post?
- Most posts go out around 17:00 UTC, and Wednesday is its busiest day, at roughly 2 posts per day across the measured window.