Logan Kilpatrick works on Gemini, Google AI Studio and the Gemini API at Google, so this is a first hand figure, though Google gave no breakdown. Gemini 4 Argon is Google's latest flagship model, covered in several recent decks.

Shortly after midnight on October 21, Logan Kilpatrick posted that in just the first 72 hours since Gemini 4 Argon launched, Google processed about 25 trillion tokens. He said the demand for intelligence is incredible and still under appreciated.

A token is a small chunk of text, roughly three quarters of a word on average in English, so 25 trillion tokens is a very large amount of input and output. Spread evenly, it would be more than 8 trillion tokens a day.

He did not say whether the figure covers only the API and AI Studio or also the Gemini app, how much was input versus output, or how it compares with earlier Gemini launches. Without that, the number is hard to compare with figures other labs have shared.

Google has spent the days since launch promoting Argon with demos built in its Antigravity coding tool, such as a 3D fur engine, a Wizard Chess game and a periodic table, and expanded access to Google AI Pro subscribers in AI Studio.

Token counts are a common way for labs to signal demand, and they also reflect how much compute a model is using.

Watch for Google to share fuller usage numbers for Gemini 4 Argon, for example on its next earnings call, including how they compare with earlier Gemini models. Rate limits or capacity problems would show whether demand is straining its compute. Also watch whether OpenAI and Anthropic respond with usage figures of their own for their latest models. Developers' reports of speed and reliability will add context to the figure.

Motubrain describes itself as building a World Action Model for robotics, a model that predicts how the world changes and how a robot should act. The model specifications in its images are the company's own and have not been checked.

On October 21, Motubrain posted that starting today, Motubrain 2.0 is livestreaming the full physical AI training pipeline continuously for half a month, and invited people to follow the training, testing and capability development of the soon to be open source model.

It said viewers will see how training stages connect and capabilities evolve, how an agent plans tasks and adjusts its actions based on feedback, whether the model can transfer existing capabilities to tasks unseen in training, and whether AI can achieve self improvement through RSI exploration, meaning recursive self improvement. It added that research rarely follows a straight path.

The images give more detail. One asks how well a model with 20 billion parameters and 500,000 hours of embodied data performs when deployed right after pre training, and lists an agenda of pre training, post training, agent work and RSI exploration. A closing note says the team began rethinking physical AI after the release of GPT 6 Astra, and asks how world action models at scale could fill the gap in Astra's fast, intuitive System 1 abilities.

The post did not include a link to the stream, a release date for the weights or a license. Livestreamed training is rare, and the promised open release is so far a plan rather than a release.

Watch for the livestream to show real results, such as robot tests on tasks unseen in training, and for Motubrain to say when the weights will be released and under what license. Researchers may compare its world action model with other open robotics models. Also watch whether its RSI exploration produces measurable gains or remains a stated goal. The stream's length and viewer numbers will show how much interest it draws.

Brian Roemmele is a commentator and investor who often promotes products he uses. His post is promotional and secondhand on how the X Money connection works, and the quoted post links to a bot he built, which he says uses example data in the screenshot.

Early on October 21, Brian Roemmele posted New X Money Grok Bot, saying few understand how powerful it is to have Grok Bot understand your finances and goals, that the X Money MCP connection to Grok Bot is just the beginning, and that it produces reports like you never seen in any bank statement.

He was quoting his own earlier post, which describes a bot that every Monday reads an X Money account and sends a weekly report. It lists balances, transactions sorted into categories, top spending and trends, cash back and interest, flagged recurring subscriptions, three habit tips and progress toward savings and investing goals, delivered as a dashboard and a PDF.

That post says the bot then asks about goals and can set up automatic investing in Coinbase. It prepares the buy, the user taps Approve, and only then is the order placed. He wrote that the bot only reads the account by default and that no money moves without approval.

The screenshot is a sample report that he labels as example data. MCP, the Model Context Protocol, is a standard for connecting AI agents to outside services.

Roemmele is promoting his own bot, and this deck could not confirm details of the X Money connection from X or SpaceXSI.

Watch for X or SpaceXSI to announce the X Money connection for Grok Bot officially, with details on what data it can read and what safeguards apply. Users trying the bot will show whether the reports are accurate. Also watch for regulators and banks to weigh in as AI agents gain access to payment and investment accounts. Any official X Money documentation would clarify how the connection works.

This is a teaser from NVIDIA's official account with no details. A petaflop is a thousand trillion operations per second, and NVIDIA has used that figure for compact AI computers before.

On October 21, NVIDIA posted a video with the caption 1 petaflop.

The 23 second clip shows chief executive Jensen Huang in his black leather jacket on a dark stage, holding a thin black device as he speaks. Beside him, a row of thin silver and white devices stands upright on a table.

The post does not name a product, give a date or say what the devices are, and the clip does not show any specifications. It is not clear from the post whether the footage is new or from an earlier event.

A petaflop of AI computing is a level NVIDIA has advertised before for small desktop systems built for AI developers, typically quoted at low precision. Teasers like this one often come ahead of launches, but readers should not assume a specific product until NVIDIA announces one.

The post drew attention mainly because of NVIDIA's reach, and it is ranked low here because it contains no information beyond the figure.

Watch for NVIDIA to name the product behind the teaser, its price and when it ships, and for the precision at which the 1 petaflop figure is measured. Also watch whether the devices in the clip are new laptops or desktop AI computers, and how they compare with Apple and AMD machines aimed at AI developers. Until then, treat the teaser as marketing.