Google AI engagement report
@GoogleAI - 2.5M followers on X
Measured over 14 original posts from a 30-day window, last computed on October 25, 2026.
Engagement
A typical post picks up 1.2K interactions against 2.5M followers, an engagement rate of 0.049%. Posts are seen about 105K times each, and 1.16% of those impressions turn into an interaction. That is about 4.25% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 0.63 post a day over the last 30 days, though only 40% 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 14 posts sampled, 57% carry an image or video, 21% are part of a thread and 43% link out. The account's strongest tracked post pulled 34K interactions, about 28x its own typical post.
Measured over 14 original posts from a 30-day window, last computed on October 25, 2026.
Where this sits in the catalog
At 0.049%, Google AI sits above the 25th percentile of the 204,424 accounts in this comparison. That places it in the below the median band, which runs 0.024% to 0.143%.
Show the percentile table
| Percentile | Engagement rate |
|---|---|
| 10th percentile | 0.004% |
| 25th percentile | 0.024% |
| 50th percentile | 0.143% |
| 75th percentile | 0.677% |
| 90th percentile | 2.55% |
| 99th percentile | 82.4% |
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% | 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 | 57% of posts | +111% | +108% to +115% | 34K |
| Outbound link | 43% of posts | -41% | -42% to -40% | 32K |
| Typical length | - | +15% | +14% to +16% | 32K |
- 57% 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.
- 43% of its posts carry a link off X. Across the catalog, posts with an outbound link run 41% below the same accounts' other posts, so a large share of this account's output sits in the weakest bucket we measure.
- Its average post runs 360 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
- Sep 10, 202628x their median
I gave the fly brain $100 to trade bitcoin. Dopamine neurons are stimulated when the fly makes profit. Neuron activity controls buy/sell decisions and makes trades on coinbase. Will the fly get rich? https://t.co/XVWSFattGu
- Sep 30, 202614x their median
Announcing Gemini 4 Argon, our new frontier model. Argon is built to sustain deep reasoning across complex, long-horizon workflows and delivers frontier performance in complex workflows across real-world software engineering, enterprise knowledge work like legal and finance, and cybersecurity defense. We’re also expanding the model’s output token limit to an industry-leading 1M tokens. Argon is currently rolling out to a set of trusted cyber defenders in the Fairwind Program, with broader availability as soon as possible.
- Sep 10, 20267.0x their median
Oh, so this is why we mapped out all 166,000 of the male fruit fly's neurons. Check out the big community effort to show just how much these tiny fly brains are capable of 🪰🧵
- Jul 30, 20265.3x 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, 20265.4x 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, 20264.0x their median
Gave google omni a sketched camera path and asked it to generate drone POV footage. https://t.co/cQZFMtOkEi
- Jun 9, 20263.4x 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, 20263.1x 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
- Sep 15, 20263.1x their median
Introducing our most advanced Gemini Audio models yet 🗣 Gemini 3.8 Live and 3.8 Live Extended Thinking let you speak, collaborate, and execute tasks seamlessly, meaning conversing with AI just got a lot more natural. So, what’s the difference between these two models? Let’s break it down: — Gemini 3.8 Live is built for scale, speed, and cost efficiency. It can handle mid-sentence interruptions, transitions across 97 languages on the fly, and understands visual context. Figure out how to fix a broken bike chain, or deal with a leaky pipe just by pointing your camera at the problem area in Search Live for step-by-step audio instructions. — Gemini 3.8 Live Extended Thinking goes one step further to bring increased intelligence to your most complex tasks. It reasons and speaks in parallel, even narrating its progress as it works. This lets it handle multi-step, behind-the-scenes projects, like planning an event, without ever losing the conversational flow. Watch how Gemini 3.8 Live combines real-time video and voice inputs in Search Live to tackle hands-on DIY plumbing tasks step by step 👇
- Jul 29, 20262.7x their median
https://t.co/AhuQq9za6Y
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.
Buy or sell Twitter (X) accounts - escrow-protected
PlayerSells is an escrow marketplace for Twitter (X) accounts. Every deal is protected, with no middleman risk.
Reading these numbers
A typical post picks up 1.2K interactions against 2.5M followers, an engagement rate of 0.049%. Posts are seen about 105K times each, and 1.16% of those impressions turn into an interaction. That is about 4.25% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 0.63 post a day over the last 30 days, though only 40% 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 14 posts sampled, 57% carry an image or video, 21% are part of a thread and 43% link out. The account's strongest tracked post pulled 34K interactions, about 28x its own typical post.
- What is Google AI's engagement rate on X?
- Google AI (@GoogleAI) has an engagement rate of 0.049%, based on the median interactions across 14 original posts from the last 30 days against 2,477,648 followers. Replies, reposts and quote-posts of other people are excluded from that sample.
- Is that a good engagement rate?
- At 0.049%, Google AI sits above the 25th percentile of the 204,424 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?
- There is not yet enough sample to rank this account against others of its size.
- When does @GoogleAI post?
- Most posts go out around 15:00 UTC, and Tuesday is its busiest day, at roughly 0.63 posts per day across the measured window.