Social Capital engagement report
@socialcapital - 184K followers on X
Measured over 21 original posts from a 30-day window, last computed on October 18, 2026.
Engagement
A typical post picks up 28 interactions against 184K followers, an engagement rate of 0.016%. Measured over 21 original posts, its engagement rate beats 29% of 21,185 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 5.5K times each, and 0.511% of those impressions turn into an interaction. That is about 2.97% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 1.7 posts a day over the last 30 days, with activity on roughly 57% of days. Most posts go out around 22:00 UTC, and Monday is the busiest day of the week. Of the 21 posts sampled, 86% carry an image or video and 90% are part of a thread. The account's strongest tracked post pulled 2.8K interactions.
Measured over 21 original posts from a 30-day window, last computed on October 18, 2026.
Compared with accounts its own size
Social Capital's engagement rate beats 29% of the tracked X accounts closest to it in follower count (21,185 accounts, accounts of similar size (decile 9 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 26% 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 0.016%, Social Capital sits above the 10th percentile of the 212,349 accounts in this comparison. That places it in the bottom 25% band, which runs below 0.025%.
Show the percentile table
| Percentile | Engagement rate |
|---|---|
| 10th percentile | 0.004% |
| 25th percentile | 0.025% |
| 50th percentile | 0.146% |
| 75th percentile | 0.696% |
| 90th percentile | 2.61% |
| 99th percentile | 83.1% |
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 22:00 UTC, and Monday 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% | 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 UTC | 0% | 116K |
| 22:00 UTC | -2% | 100K |
| 23:00 UTC | -1% | 90K |
Show engagement by day of week as a table
| Day | Vs author median | Posts |
|---|---|---|
| Sunday | +5% | 393K |
| Monday | +1% | 483K |
| Tuesday | -2% | 520K |
| Wednesday | -3% | 472K |
| Thursday | -2% | 430K |
| Friday | -3% | 447K |
| Saturday | +2% | 393K |
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.
| Format | This account | Catalog effect | 95% interval | Accounts behind it |
|---|---|---|---|---|
| Image or video | 86% of posts | +111% | +108% to +115% | 34K |
| Outbound link | 0% of posts | -41% | -42% to -40% | 32K |
| Typical length | - | +15% | +14% to +16% | 32K |
- 86% 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.
- 0% 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 290 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
- Feb 10, 2026
In 2020, I hired McKinsey to teach me about the global energy transition. I learned a lot, but it cost me more than $2M. After that experience, I decided to build my own internal research project at Social Capital called Learn With Me. Over the last two years, my @socialcapital team and I systematized our process into a product that allows anyone to learn with me continuously, for a fraction of the cost of a single McKinsey report. Each Learn With Me Deep Dive represents 300+ hours of first-principles research, expert interviews, and truth-seeking analysis of what's happening in the world today. We have 3 main goals: 1) Educate: Break down complex topics from first principles 2) Contextualize: Show why it matters now in a shifting world 3) Analysis: Help individuals, founders, investors, and policymakers make informed decisions on their own Over the last 2 years, we’ve released 30+ Deep Dive reports across five primary themes I believe are essential to understanding the world today: Technology, Energy, Life Sciences, Economic Analysis, and Socio-Political Trends. Some of our most popular Deep Dives: - Stablecoins: Early last year, I predicted that the biggest business winner in 2025 will be dollar-denominated stablecoins and shared the research behind this thesis. - Magnificent 7: They are the undisputed winners of the internet era, with over $21T in combined market cap. We took a deeper look at each company’s products, key competitive edge, and revenue pillars. - China: We created a 3-part, 450-slide report to explore the history, governance, and both the domestic and foreign policies of one of the world’s most powerful and longest-lasting civilizations. Historically, we averaged ~1 comprehensive Deep Dive per month, delivered as a slide deck. After expanding our team, we’re looking to scale our output significantly in 2026: - 4X Deep Dives per month: You'll start to see them drop weekly instead of monthly, and we aim to raise the quality bar with each release. - Explainer Videos: We’ll be experimenting with cinematic explainer videos for our Deep Dive topics, presented by me with high-quality animations to make the research more accessible and shareable. - Community: We aim to deepen our community of curious individuals who are invested in the topics we cover with our group chat that I’m involved in, and we plan to do even more next year to cultivate digital and physical connections between our members. Price: While we’re increasing output by 4-5x, we will be keeping the price at $99/m or $999/y. Essentially, we aim to curate a community of individuals who are curious and invested in the topics we cover. Those who want to learn alongside other like-minded members and me can self-select. Deep Dives we have planned for Q1 2026: Humanoid Robotics, Autonomous Investing, Tokenization of Equities, Critical Minerals and Rare Earths, Prediction Markets, American AI Stack, Batteries, Population Collapse, Longevity and Aging, AI in Healthcare, GLP-1s, Affordable Housing, Neuralink, Autonomous Driving, California Fiscal Review, Agentic AI. We will also look to our subscribers to influence what we research in our private group chat. I invite you to come and Learn With Me here: https://t.co/PSBNs9US6o
- Oct 5, 202648x their median
~90% of frontier lab compute now goes to post-training and inference. https://t.co/vTxECVXYRQ
- Apr 4, 2023
Annual letter drop… 2022 edition --Returns --What the hell is going on (end of ZIRP, Ukraine, re-rating growth assets) --How we think about the future --Portfolio update --SPAC post-mortem Read it here: https://t.co/FCISD3mhw6
- Mar 1, 2024
Some news! :) https://t.co/3iOHcYvq04
- Mar 12, 2024
We have terminated the employment of two of our employees due to employee-specific circumstances. We have no further comments at this time.
- Feb 27, 2024
That's a strong-looking GroqRack™ right there, don't ya think? Serving up tokens faster than anyone. We're building A LOT more hardware and increasing capacity weekly. Scaling to be a token factory to help change the world of AI through the world's greatest inference engine. https://t.co/l3q32XjC6b
- Apr 4, 2023
Our 2022 annual letter is out, including our views on the year past and how it shapes how we see the future... https://t.co/Wj3ArEBJtl
- Jul 19, 2026
ASML is the only company capable of manufacturing the machine that creates every advanced AI chip in the world. Yet over the last five years, ASML is up ~120%, while Nvidia is up ~1,300%. This means that even though ASML is the most unavoidable bottleneck in the AI stack, Nvidia still outperformed it by ~10x. To understand where value actually accrues in the AI stack, our team at Social Capital set out to map it: The stack we drew has six layers: -Infrastructure -Chips -Data -Models -Execution -Application Each layer has its own “fulcrum assets,” which are single points in the stack that every unit of value has to cross. The companies that sit on the less obvious ones will likely shape the next 40 years of computing. Infrastructure is the most concentrated layer in the stack. In fact, four companies in Japan supply the film that every chip needs, and a single mine in North Carolina sits underneath most wafers in production. In 1880, Rockefeller owned 90% of refining. This same pattern is forming now. After infrastructure is where the stack forks into Digital and Physical AI. Digital AI runs in the cloud, trains on text, and executes through APIs. Physical AI runs on local chips, trains on real-world data, and executes through motors and batteries. These two forks compound on very different curves, and a handful of names already sit on the boundaries that matter. Every era of computing has been won by the people who got the stack right. This is the clearest view we have of where AI is going.
- Jul 20, 20264.7x their median
Texas and the Midwest are about to capture more than half of all of the new US AI infrastructure development: Amazon, Google, Meta, and Microsoft are expected to spend nearly $700B combined in 2026 on AI infrastructure, up more than 60% from 2025. Where this money is going exactly is to AI data center real estate that houses the specific infrastructure needed to train, deploy, and deliver AI apps and services. This requires 3 things: - A dedicated grid connection of at least 100 megawatts (training requires more power). - A land parcel of 100 acres or more. - A cool climate that reduces the cost of cooling (chips heat up often). Established markets like Northern Virginia and Silicon Valley have run out of available power, and that is pushing new construction inland into places like Texas. The thing that’s going to determine how suitable a place is for AI infrastructure is going to be based on these 6 things: - Power infrastructure (30%) - Land availability (20%) - Network connectivity (15%) - Labor market (15%) - Business environment (10%) - Climate and resources (10%) And based on these markers, here’s what the top markets look like in the US on a score range of 2.9-4.2:
- Oct 12, 20264.4x their median
The two constraints on AI are effectively electricity and chips. China and the U.S. have different advantages regarding both: - China wins on raw electric power (added 543 GW of new capacity last year), but their ability to produce advanced chips is still a few years behind. - The U.S. is ahead on chips, dependent on an international supply chain. NVIDIA and AMD design leading processors, while Taiwan’s TSMC manufactures many of them. This means access to Taiwanese factories is a critical part of American AI leadership. The bottleneck for America is clear: we are short on electricity.
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 28 interactions against 184K followers, an engagement rate of 0.016%. Measured over 21 original posts, its engagement rate beats 29% of 21,185 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 5.5K times each, and 0.511% of those impressions turn into an interaction. That is about 2.97% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 1.7 posts a day over the last 30 days, with activity on roughly 57% of days. Most posts go out around 22:00 UTC, and Monday is the busiest day of the week. Of the 21 posts sampled, 86% carry an image or video and 90% are part of a thread. The account's strongest tracked post pulled 2.8K interactions.
- What is Social Capital's engagement rate on X?
- Social Capital (@socialcapital) has an engagement rate of 0.016%, based on the median interactions across 21 original posts from the last 30 days against 184,264 followers. Replies, reposts and quote-posts of other people are excluded from that sample.
- Is that a good engagement rate?
- At 0.016%, Social Capital sits above the 10th percentile of the 212,349 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 @socialcapital have real engagement?
- Its engagement rate beats 29% of the tracked X accounts closest to it in follower count (21,185 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 @socialcapital post?
- Most posts go out around 22:00 UTC, and Monday is its busiest day, at roughly 1.7 posts per day across the measured window.