Eric Schmidt engagement report
@ericschmidt - 2.0M followers on X
Measured over 1 original post from a 30-day window, last computed on October 31, 2026.
Engagement
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 646 interactions against 2.0M followers, an engagement rate of 0.032%. Posts are seen about 120K times each, and 0.537% of those impressions turn into an interaction. That is about 5.91% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 0.13 posts a day over the last 30 days, though only 10% of days saw any activity at all. Most posts go out around 16:00 UTC, and Wednesday is the busiest day of the week. Of the 1 posts sampled, 100% carry an image or video. The account's strongest tracked post pulled 7.6K interactions, about 13x 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 October 31, 2026.
Where this sits in the catalog
At 0.032%, Eric Schmidt sits above the 25th percentile of the 217,745 accounts in this comparison. That places it in the below the median band, which runs 0.025% to 0.149%.
Show the percentile table
| Percentile | Engagement rate |
|---|---|
| 10th percentile | 0.004% |
| 25th percentile | 0.025% |
| 50th percentile | 0.149% |
| 75th percentile | 0.704% |
| 90th percentile | 2.63% |
| 99th percentile | 82.5% |
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 16:00 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% | 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 | 100% of posts | +111% | +108% to +115% | 34K |
| Outbound link | 0% of posts | -41% | -42% to -40% | 32K |
| Typical length | - | +15% | +14% to +16% | 32K |
- 100% 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 1569 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
- May 6, 202613x their median
We are back. After one year of quiet building. Introducing GENE-26.5, our first robotic brain that takes a major step toward human-level capability. For years, robotics has struggled to learn from the world’s largest and valuable data source: Humans. Solving it means rethinking the whole stack from the ground up: - A robotics-native foundation model. - A 1:1 human-like robotic hand. - A noninvasive data collection glove for motion, force, and touch. - A simulator that turns weeks of experiments into minutes. GENE-26.5 is trained across language, vision, proprioception, tactile, and action. We designed a set of tasks to test how far we can go with this new paradigm. Fully autonomous, 1x speed, one model, same weights. (Enjoy with sound on) We are approaching the endgame for robotics. And this is just a beginning.
- May 1, 2025
Today, we are launching the first publicly available AI Scientist, via the FutureHouse Platform. Our AI Scientist agents can perform a wide variety of scientific tasks better than humans. By chaining them together, we've already started to discover new biology really fast. With the platform, we are bringing these capabilities to the wider community. Watch our long-form video, in the comments below, to learn more about how the platform works and how you can use it to make new discoveries, and go to our website or see the comments below to access the platform. We are releasing three superhuman AI Scientist agents today, each with their own specialization: A general-purpose agent (Crow); An agent to automate literature reviews (Falcon); and An agent to answer the question “Has anyone done X before” (Owl). We are also releasing an experimental agent, Phoenix, that has access to a wide variety of tools for planning experiments in chemistry. More on that below. The three literature search agents (Crow, Falcon, and Owl) have benchmarked superhuman performance. They also have access to a large corpus of full scientific texts, which means that you can ask them more detailed questions about experimental protocols and study limitations that general-purpose web search agents, which usually only have access to abstracts, might miss. Our agents also use a variety of factors to distinguish source quality, so that they don’t end up relying on low-quality papers or pop-science sources. Finally, and critically, we have an API, which is intended to allow researchers to integrate our agents into their workflows. Phoenix is an experimental project we put together recently just to demonstrate what can happen if you give the agents access to lots of scientific tools. It is not better than humans at planning experiments yet, and it makes a lot more mistakes than Crow, Falcon, or Owl. We want to see all the ways you can break it! The agents we are releasing today cannot yet do all (or even most!) aspects of scientific research autonomously. However, as we show in the video, you can already use them to generate and evaluate new hypotheses and plan new experiments way faster than before. Internally, we also have dedicated agents for data analysis, hypothesis generation, protein engineering, and more, and we plan to launch these on the platform in the coming months as well. Within a year or two, it is easy to imagine that the vast majority of desk work that scientists do today will be accelerated with the help of AI agents like the ones we are releasing today. The platform is currently free-to-use. Over time, depending on how people use it, we may implement pricing plans. If you want higher rate limits, especially for research projects, get in touch. @m_skarlinski, @andrewwhite01, @_tnadolski, Remo Storni, @semajazarb, @ludomitch, @MichaelaThinks, as well as @jasonjoyride and his team for making such fantastic videos of us!
- Nov 12, 20256.1x their median
“Creativity is intelligence having fun.” Unleash your creativity and imagination with Marble - our 3D world generation model, now available to everyone!
- Oct 9, 20252.6x their median
🪩The one and only @stateofai 2025 is live! 🪩 It’s been a monumental 12 months for AI. Our 8th annual report is the most comprehensive it's ever been, covering what you *need* to know about research, industry, politics, safety and our new usage data. My highlight reel:
- May 15, 20252.4x their median
I sat down with @BilawalSidhu at @TEDTalks to share why the AI revolution is underhyped. This is the most important development in the last 500-1,000 years. We’re entering a new era, it’s coming fast, and we can't screw it up. Watch here: https://t.co/6JhwSUDxeW
- May 27, 20262.2x their median
We are back again :) After three weeks of quiet building. Introducing Genesis World 1.0, our latest simulation platform, the second release in our full-stack suite. Open-sourced. Robotics is still bottlenecked by the 1× speed of the physical world. Every model, checkpoint, and data recipe eventually needs to be tested on physical hardware, slowly, expensively, and with limited coverage. One hour in reality can become 100 days in simulation. That is how robotics model iteration moves from a wall-clock bottleneck to a compute problem. To make this work, simulation has to be both fast and trustworthy. Over the past year, we rebuilt the entire stack: a GPU-accelerated cross-platform compiler, penetration-free multi-physics contact solvers, unified rigid and deformable physics, and a photo-realistic renderer purpose-built for physical AI applications. We built Nyx, a high-performance path-traced rendering engine for robotics application. Genesis World 1.0 achieves near realtime performance with our latest development for penetration-free IPC solver, supporting various types of deformables beyond rigid bodies. It supports contact-rich, dexterous manipulation simulation across different embodiments: unitree, sharpa, wuji, genesis hand and various types of grippers. Under the hood is Quadrants, our effort in pushing forward cross-platform GPU-accelerated computation. Quadrants started as a fork of Taichi, and we rebuilt most of the critical parts for optimizing simulation workloads, giving 10x faster launch time and up to 4.6x runtime performance compared to the initial Genesis release. Together, they bring us to an unprecedentedly low sim-to-real gap, enabling zero-shot real-to-sim model evaluation and much faster iteration of GENE. All available today. Genesis World 1.0: https://t.co/aknCM3eqws Quadrants: https://t.co/uXqPNI4cb6 Nyx: https://t.co/R8j0djqGnV
- May 7, 20251.6x their median
I don’t think the next big leap in software productivity comes from “vibe coding.” It comes from removing the grind in real codebases.@AugmentCode’s new Remote Agent tackles flaky tests, stale docs & tedious refactors—with up to 10 autonomous agents. Learn more: https://t.co/ESPfzLymO1
- Dec 17, 2025
The US might run out of energy for AI data centers by 2028, here is my attempt to help solve this problem. We are just getting started ! https://t.co/t7ocoF6BhL
- Dec 10, 20252.4x their median
Very happy to announce we have also used our @Nvidia H100 on Starcloud-1 to run inference with @GoogleDeepMind's Gemma model - the open source version of Gemini. These are Gemma's first words in space. << Greetings, Earthlings! Or, as I prefer to think of you – a fascinating collection of blue and green. Let’s see what wonders this view of your world holds. I’m Gemma, and I’m here to observe, analyze, and perhaps, occasionally offer a slightly unsettlingly insightful commentary. Let’s begin!
- Aug 5, 2025
AI revolutionized coding. Security hasn't kept up–until now. Introducing @CorridorSecure: the future of secure coding. We just raised a $5.4M seed from @Conviction and hired @alexstamos. Corridor is trusted by leading companies like @cursor_ai–and we’re just getting started. 🧵 https://t.co/VYh7MvolBt
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 646 interactions against 2.0M followers, an engagement rate of 0.032%. Posts are seen about 120K times each, and 0.537% of those impressions turn into an interaction. That is about 5.91% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 0.13 posts a day over the last 30 days, though only 10% of days saw any activity at all. Most posts go out around 16:00 UTC, and Wednesday is the busiest day of the week. Of the 1 posts sampled, 100% carry an image or video. The account's strongest tracked post pulled 7.6K interactions, about 13x 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 Eric Schmidt's engagement rate on X?
- Eric Schmidt (@ericschmidt) has an engagement rate of 0.032%, based on the median interactions across 1 original posts from the last 30 days against 2,036,086 followers. Replies, reposts and quote-posts of other people are excluded from that sample.
- Is that a good engagement rate?
- At 0.032%, Eric Schmidt sits above the 25th percentile of the 217,745 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 @ericschmidt have real engagement?
- There is not yet enough sample to rank this account against others of its size.
- When does @ericschmidt post?
- Most posts go out around 16:00 UTC, and Wednesday is its busiest day, at roughly 0.13 posts per day across the measured window.