Antonio Grasso engagement report
@antgrasso - 351K followers on X
Measured over 21 original posts from a 30-day window, last computed on October 6, 2026.
Engagement
A typical post picks up 167 interactions against 351K followers, an engagement rate of 0.048%. Measured over 21 original posts, its engagement rate beats 58% of 20,444 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 4.2K times each, and 3.94% of those impressions turn into an interaction. That is about 1.21% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 2.3 posts a day over the last 30 days, with activity on almost every day in the window. Most posts go out around 13:00 UTC, and Tuesday is the busiest day of the week. Of the 21 posts sampled, 86% carry an image or video and 19% link out. The account's strongest tracked post pulled 239 interactions. Recurring topics include #ai, #cryptoexplained, #energy.
Measured over 21 original posts from a 30-day window, last computed on October 6, 2026. Recurring tags: #ai, #cryptoexplained, #energy.
Compared with accounts its own size
Antonio Grasso's engagement rate beats 58% of the tracked X accounts closest to it in follower count (20,444 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 85% 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 0.048%, Antonio Grasso sits above the 25th percentile of the 205,048 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 | 0.678% |
| 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 13:00 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 | 86% of posts | +111% | +108% to +115% | 34K |
| Outbound link | 19% 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.
- 19% 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 958 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 5, 2026
Let AI agents coordinate the workflow and handle more of the execution. But do not confuse autonomy with accountability: however much autonomy they are given, responsibility for the process and its outcome remains with the human process owner. By design. Microblog @antgrasso The job changes. Imagine an order process that crosses sales, finance, and operations. Today, a process owner may spend time keeping handoffs moving and resolving exceptions between teams. An AI agent could coordinate much of that flow automatically. So what is left for the process owner? Quite a lot. Someone still has to define what the agent is allowed to do and when a case needs human judgment. Someone must also decide whether the outcome remains acceptable when business conditions change. The process owner therefore moves from coordinating every handoff to governing the mandate behind the process. AI can run more of the flow. It cannot accept responsibility for what the flow produces. #AIAgents #ProcessManagement
- Aug 30, 2026
Digital transformation often fails because we look for technology before we look inside an organization and see how work really happens. The people doing the work can reveal friction that plans and process maps often miss until implementation begins. Microblog by @antgrasso Before redesigning a workflow, watch it. A new AI-enabled process may look efficient in a project plan. The people doing the work may know that customer exceptions arrive outside the system or that one handoff already creates delays. Those details can change the design. What happens if you ask the people who do the work before asking them to adapt to the new way of working? Observe how the work happens today. Listen where people expect friction. Their suggestions can help refine the redesign before it reaches a larger part of the organization. Then test it with them. Digital transformation is easier to scale when reality has already had a chance to challenge the design. Before changing how people work, look inside and listen to the people who already know where the friction lives.
- Sep 28, 2026
If you think AI agents can negotiate without human direction and oversight, good luck. Autonomy without boundaries is called loss of control. Let agents move, but keep the mandate visible and human intervention ready when the negotiation moves beyond it. Microblog @antgrasso Take a procurement agent negotiating with the agent of a supplier. It can adjust delivery dates or discounts within an agreed mandate. Fine. But the mandate is only the starting point. If the negotiation moves toward a condition the agent was not authorized to accept, someone needs to see it and be able to take over. That does not mean a human must approve every move. Otherwise, autonomy disappears. It means the organization knows where the agent can act and when human judgment must return. After the agreement, people should also be able to reconstruct why it was reached. Let the agents negotiate. Keep humans in command of the authority they delegate. Autonomy should reduce human intervention, not human responsibility. #AIAgents #AIGovernance #AgentOps
- Aug 24, 2026
AgentOps is the discipline that keeps AI agents visible and under control once they begin working inside real business processes. An agent may perform well during a pilot and still behave differently in production. Data changes. Workflows are updated. Microblog by @antgrasso A permission that looked harmless during testing may create risk when the agent starts acting across live systems. Consider an AI agent that updates supplier records. Before deployment, the company defines which fields it can change. Its behavior is tested with realistic cases, including incomplete information and unexpected requests. Once the agent is active, the work continues. Teams need to see what it did and where review is required. When behavior moves outside accepted limits, they must be able to adjust the agent or stop it. This is the purpose of AgentOps: turning autonomous activity into something the organization can observe and manage with confidence. An AI agent should never be easier to deploy than to stop.
- Aug 29, 2026
Still expecting the CIO to say “mea culpa” when resilience fails? You may have once. Today, digital resilience is a board responsibility because disruption reaches operations and business continuity, far beyond the data center and IT function alone. Microblog by @antgrasso Why has the responsibility moved? Because a digital failure rarely stays inside IT. If a cloud outage stops online orders, restoring the infrastructure is a technology problem. Deciding which business services must return first is an executive leadership decision. If a cyberattack forces a factory to slow production, the CIO can coordinate recovery of the systems. But who decides how long operations can tolerate the disruption? Who decides what customers should be told and which business risks are acceptable? Executive leadership needs to make those decisions. Digital resilience now depends on knowing which operations the company cannot afford to lose and making sure technology recovery supports them. The CIO remains central, but cannot carry that responsibility alone. And resilience starts before the incident. Investment choices and continuity plans need agreement while everything is still working. Accountability must already be clear. When disruption arrives, the CIO can restore technology. Executive leadership must already know how the business will continue.
- Sep 6, 2026
The best employee experience? Probably not humans competing with machines. Better to design work so each does what it does best: let AI handle speed and repetition, while people keep judgment and responsibility in the decisions shaping the final outcomes. Microblog @antgrasso Think of a claims team. AI can sort incoming cases and summarize the documents. Good. Let it do that. The specialist can spend more time on the cases where context changes the interpretation or an exception deserves attention. But if the old workflow stays the same, something goes wrong. People may end up reviewing the AI output and still completing all the steps they were doing before. Faster technology. More work for the employee. Not exactly progress. Work design has to decide where machine speed removes effort and where human judgment stays in charge. People should also know when they need to intervene and what they remain responsible for. AI should take friction out of work, not give people another layer to manage. A better employee experience is not less human work. It is less pointless work. #EmployeeExperience #HumanCenteredAI
- Oct 3, 2026
You found a new AI model with billions of parameters and you like what you see, but now you are wondering whether it is also efficient. Before getting excited about the size, ask one more question: what does it take to run that model at the scale you need? Microblog @antgrasso That number tells you only part of what matters. Parameters are numerical values learned during training. More of them can increase what a model is capable of doing, but they also have to be stored and used when the model runs. That requires memory and computation. How long does the model take to produce an answer? How much energy does it use? What does running it cost at the scale you need? Efficiency also depends on the workload. A model can be a sensible choice for one task and unnecessarily demanding for another. The same model can behave differently depending on the hardware and how it is deployed. So parameter count can tell you something about model size and capacity. It cannot tell you, by itself, whether the model is efficient for the job you need to do. Big numbers may impress. Efficiency is about the resources required to get the result you need. #AIEfficiency
- Sep 4, 2026
Power is nothing without control. With autonomous AI agents, that old slogan sounds less like advertising and more like a business reality. Autonomy can accelerate work, but without boundaries it can accelerate mistakes just as quickly at scale, too. Microblog by @antgrasso What does control look like in practice? Take an AI agent handling customer refunds. A request follows company policy and stays within its approved limit. The agent acts. The amount exceeds its threshold or the case is ambiguous. It escalates to a person. The requested action is prohibited. It stops. Act. Escalate. Stop. Those boundaries have to exist before deployment, because an autonomous agent should not invent its own limits while working at machine speed. Permissions and thresholds define where the agent can operate. Traceability then lets the organization see what happened and review the outcome when needed. Accountability stays with the people and the organization that designed the system and accepted its use. Autonomy depends on the machine knowing where its authority ends. #AIAgents #AgentOps
- Sep 19, 2026
Blockchain is to crypto what the internet is to email. One made the other famous, but the technology can do much more. Crypto is one application; blockchain can also help organizations share records and verify information across companies without one owner. Microblog @antgrasso Forget crypto for a moment. Imagine three companies working on the same supply chain. Each keeps its own records, so they spend time checking which version is correct. With blockchain, they can share a record of what happened. Each update follows agreed rules, and previous entries are difficult to change without the others noticing. A product can carry a shared history as it moves between companies. A digital credential can be issued by one organization and verified by another. A smart contract can trigger an action when a defined condition is met. Crypto uses the same technology to record and transfer digital value. It is one application, not the definition of blockchain. The question is: do several parties need to share and verify the same record without giving one of them control? If yes, blockchain may be worth considering. #Blockchain #DigitalTrust
- Sep 22, 2026
You have heard that AI consumes energy. Fine. But what exactly is using that energy while you type a prompt and wait for the answer? What you see on screen is only the result, so the energy behind it goes beyond the few seconds spent generating the answer. Microblog @antgrasso There is no single energy cost hiding inside an AI answer. Some electricity is already being used before your prompt arrives. The infrastructure may be powered and ready to serve requests. Then you press Enter. The model has to process your prompt before it can answer. Longer inputs, especially when they include a lot of context, can require more computation. Then comes generation. The model produces the response step by step, using computation as the output grows. Longer responses generally require more work. The model is not working alone. Memory and networking keep data moving through the system, while cooling uses electricity as the hardware runs. Which part uses the most energy? There is no fixed answer. It depends on the model and the workload. Model size, prompt and response length, modality, and infrastructure efficiency can all change the total. That is why saying “one AI prompt consumes X” can hide more than it explains. The energy is in the whole slice. Like a cheesecake, you enjoy the result without necessarily thinking about what went into every layer. #ArtificialIntelligence #EnergyEfficiency
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.
Recurring topics
The most frequent hashtags in the sampled posts. They describe what this account writes about; they are not a performance signal, and the catalog-wide breakdown on the hub shows how little hashtag count moves.
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Reading these numbers
A typical post picks up 167 interactions against 351K followers, an engagement rate of 0.048%. Measured over 21 original posts, its engagement rate beats 58% of 20,444 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 4.2K times each, and 3.94% of those impressions turn into an interaction. That is about 1.21% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 2.3 posts a day over the last 30 days, with activity on almost every day in the window. Most posts go out around 13:00 UTC, and Tuesday is the busiest day of the week. Of the 21 posts sampled, 86% carry an image or video and 19% link out. The account's strongest tracked post pulled 239 interactions. Recurring topics include #ai, #cryptoexplained, #energy.
- What is Antonio Grasso's engagement rate on X?
- Antonio Grasso (@antgrasso) has an engagement rate of 0.048%, based on the median interactions across 21 original posts from the last 30 days against 351,159 followers. Replies, reposts and quote-posts of other people are excluded from that sample.
- Is that a good engagement rate?
- At 0.048%, Antonio Grasso sits above the 25th percentile of the 205,048 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 @antgrasso have real engagement?
- Its engagement rate beats 58% of the tracked X accounts closest to it in follower count (20,444 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 @antgrasso post?
- Most posts go out around 13:00 UTC, and Tuesday is its busiest day, at roughly 2.27 posts per day across the measured window.