Google AI engagement report
@GoogleAI - 2.4M followers on X
Measured over 8 original posts from a 30-day window, last computed on August 31, 2026.
Engagement
A typical post picks up 1.1K interactions against 2.4M followers, an engagement rate of 0.044%. Measured over 8 original posts, its engagement rate beats 72% of 3,739 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 111K times each, and 0.981% of those impressions turn into an interaction. That is about 4.53% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 0.43 posts a day over the last 30 days, though only 30% of days saw any activity at all. Most posts go out around 15:00 UTC, and Tuesday is the busiest day of the week. Of the 8 posts sampled, 50% carry an image or video, 50% are part of a thread and 38% link out. The account's strongest tracked post pulled 6.5K interactions, about 6.0x its own typical post.
Measured over 8 original posts from a 30-day window, last computed on August 31, 2026.
Compared with accounts its own size
Google AI's engagement rate beats 72% of the tracked X accounts closest to it in follower count (3,739 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 56% 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 0.044%, Google AI sits above the 25th percentile of the 36,134 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 | 0.431% |
| 90th percentile | 2.11% |
| 99th percentile | 161.6% |
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 15:00 UTC, and Tuesday 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% | 50K |
| 01:00 UTC | -2% | 51K |
| 02:00 UTC | -4% | 49K |
| 03:00 UTC | -4% | 53K |
| 04:00 UTC | -6% | 42K |
| 05:00 UTC | -4% | 41K |
| 06:00 UTC | -4% | 47K |
| 07:00 UTC | -5% | 51K |
| 08:00 UTC | -4% | 60K |
| 09:00 UTC | -3% | 68K |
| 10:00 UTC | -2% | 71K |
| 11:00 UTC | -3% | 77K |
| 12:00 UTC | -2% | 85K |
| 13:00 UTC | -2% | 93K |
| 14:00 UTC | -3% | 96K |
| 15:00 UTC | -2% | 99K |
| 16:00 UTC | -3% | 96K |
| 17:00 UTC | -2% | 89K |
| 18:00 UTC | -2% | 83K |
| 19:00 UTC | -2% | 79K |
| 20:00 UTC | -1% | 73K |
| 21:00 UTC | -1% | 65K |
| 22:00 UTC | -1% | 57K |
| 23:00 UTC | -2% | 51K |
Show engagement by day of week as a table
| Day | Vs author median | Posts |
|---|---|---|
| Sunday | +4% | 228K |
| Monday | 0% | 281K |
| Tuesday | -2% | 270K |
| Wednesday | -1% | 249K |
| Thursday | -2% | 242K |
| Friday | -3% | 250K |
| Saturday | +3% | 225K |
Best tweets
- Jul 30, 20266.0x their median
For decades, we’ve dreamed of robots that can seamlessly step into our world and lend a hand. Today, we take a major stride toward making that dream a reality: Introducing Gemini Robotics 2 from @GoogleDeepMind, the intelligence layer powering the next generation of truly adaptable robots. This major advance unlocks intelligent whole-body control, advanced dexterity, and even multi-robot collaboration 🤯. Ok but... how does a robot actually "think"? Real-world tasks take time and planning. To manage that complexity, our new embodied reasoning model, Gemini Robotics ER 2, acts as the robot’s high-level brain, enhancing the robot’s capabilities to: — Observe the environment — Reason about the actions needed to complete the task — Coordinate with the vision-language-action model to carry out actions — Track progress until the job is done This setup allows robots to execute complex multi-step workflows, self-correct if a step fails, and adapt to completely novel situations. Learn more about Gemini Robotics ER 2 (and our two other brand new models) here: https://t.co/1YEpoYAhww
- Jul 16, 20265.0x their median
3 years ago we started as a tiny experiment with the goal of helping you learn faster. Since then, we grew to bring audio, video, and interactivity to your sources, transitioning from a passive workspace to your true research companion. And now, notebooks have even become an entire ecosystem: you can already access them in the @GeminiApp and soon in Google Search So, with these advancements, it’s time for us to evolve once again: NotebookLM is now Gemini Notebook ✨📓 The same app you know and love isn’t going anywhere, we just have an updated name that reflects our role in Google's AI portfolio. And our mission stays exactly the same: helping you learn, faster. Thank you for believing in us— this wouldn't have been possible without your passion (and feature requests...) Big things to come (yes, even folders📂!) so stay tuned. Sincerely, The Project Tailwind team
- May 26, 20264.5x their median
Gave google omni a sketched camera path and asked it to generate drone POV footage. https://t.co/cQZFMtOkEi
- Jun 9, 20263.8x their median
Today, we released Gemini 3.5 Live Translate, our latest audio model for live speech-to-speech translation. It supports over 70 languages and starts translating as soon as you start talking, streaming translations while listening to what you say next. No awkward pauses or choppy audio, just real connection without language barriers. So, how does it work? 🤔 The model is able to make split-second decisions to juggle speed and translation quality so conversations actually feel fluid, human, and natural. In order to do this, the model must receive and contextualize the input while simultaneously outputting the translated speech. Through this process, Gemini 3.5 Live Translate manages to stay mere seconds behind each speaker and can even maintain pacing, pitch, and intonation across extended sessions. See it in action below, or try it yourself in the Google Translate app on iOS & Android.
- Jul 21, 20263.5x their median
Today, we're introducing not one but TWO new models, striking the balance between efficiency and quality to enable you to build production AI agents. — Gemini 3.6 Flash: Addresses efficiency feedback we received from Gemini 3.5 Flash with upgrades in coding, knowledge work, and multimodal tasks faster, more accurately, and with substantially fewer tokens per task — Gemini 3.5 Flash-Lite: Our fastest, most cost-effective 3.5-class model yet built for agentic workflows, hitting ~350 output tokens/sec with improved coding and overall quality Start building with these today via the Gemini API in @GoogleAIStudio or try them out in the @GeminiApp
- Jul 29, 20263.0x their median
https://t.co/AhuQq9za6Y
- Apr 30, 20262.9x their median
Last week, we made Gemini Embedding 2, our first natively multimodal embedding model, available to the general public. Since then, developers have used it to build video analysis tools, visual shopping assistants, and more. But you might be wondering... what is an embedding model? 🤔 Let’s break it down! 1. What is it? Think of an embedding model as a "universal translator." It takes text, images, video, and audio data and turns them into a long string of numbers, like a unique digital fingerprint. 2. How does it work? Historically, search has been text only. Now, instead of just matching data by keyword, Gemini Embedding 2 maps multiple modalities in the same space based on meaning. It "feels" the connection between a video of a soccer goal and the words "game-winning shot" without needing tags. For example, "ocean" and "waves" are placed close together, but "ocean" and "toaster" are miles apart. 3. How can you use it? Developers have been using it to incorporate smarter search functionality into their builds. This means creating tools where you can snap a photo of a product and type "find this in yellow," or search through thousands of hours of video by describing what happens in a scene. 4. Ready to try it out for yourself? You can start using it today via the Gemini API or the Gemini Enterprise Agent Platform.
- May 19, 20262.6x their median
By now, you've probably heard about Gemini Omni, our new model designed to create anything from any input, starting with video. But... what's the big deal? Let’s break it down 🧵👇 https://t.co/QbxMNZa2Wx
- Apr 15, 20262.4x their median
Today we launched Gemini 3.1 Flash TTS, our most expressive and controllable text-to-speech model yet. This launch [excitement] includes audio tags! 🗣🏷 Audio tags [explanatory] are a seamless way to guide vocal style, pace, and delivery using natural language commands embedded directly in your text. Want a different tempo or tone? [amazement] Just tag the audio to steer the AI-speech output! The model supports 70+ languages (24 of which are high-quality evaluated languages, including: Japanese, Hindi, and Arabic). Watch the audio tags in action in the demo below ↓
- May 26, 20262.0x their median
https://t.co/j6Qu6uAg1b
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 1.1K interactions against 2.4M followers, an engagement rate of 0.044%. Measured over 8 original posts, its engagement rate beats 72% of 3,739 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 111K times each, and 0.981% of those impressions turn into an interaction. That is about 4.53% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 0.43 posts a day over the last 30 days, though only 30% of days saw any activity at all. Most posts go out around 15:00 UTC, and Tuesday is the busiest day of the week. Of the 8 posts sampled, 50% carry an image or video, 50% are part of a thread and 38% link out. The account's strongest tracked post pulled 6.5K interactions, about 6.0x its own typical post.
- What is Google AI's engagement rate on X?
- Google AI (@GoogleAI) has an engagement rate of 0.044%, based on the median interactions across 8 original posts from the last 30 days against 2,446,947 followers. Replies, reposts and quote-posts of other people are excluded from that sample.
- Is that a good engagement rate?
- At 0.044%, Google AI sits above the 25th percentile of the 36,134 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 @GoogleAI have real engagement?
- Its engagement rate beats 72% of the tracked X accounts closest to it in follower count (3,739 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 @GoogleAI post?
- Most posts go out around 15:00 UTC, and Tuesday is its busiest day, at roughly 0.43 posts per day across the measured window.