Antonio Grasso

Interest in science and tech is far from uniform, even across major economies. For companies, this matters: you cannot assume the same curiosity toward technology in every market or workforce. Digital change starts from different points. Source @StatistaCharts via @antgrasso

Write "The team is making great..." and your mind already expects an ending. A language model gets there through a sequence of calculations. You draw on experience with language. The model uses patterns learned during training to work out what could follow. Microblog @antgrasso A computer needs numerical representations to process language, but assigning a number to a word is only the beginning. The model divides text into tokens, which may be whole words or smaller pieces. Each token receives a numerical ID, essentially a label that points to a learned vector of numbers called an embedding. Now comes the interesting part. As the model processes these vectors, it builds representations influenced by the preceding context. The same word can contribute differently depending on the sentence. After "The team is making great...", the model might assign a higher probability to "progress" than to "decisions" or "plans". It then selects a token and uses it as part of the context for predicting the next one. And the calculation continues. Change the preceding text, and those probabilities may change too. Even a small modification to your prompt can influence what the model generates. #LanguageModels #AI

An AI agent can initiate blockchain transactions and perform DAO operations, provided it has an authorized wallet and the required permissions. An agent can go beyond recommending an action and carry it out. People still decide the limits of that authority. Microblog @antgrasso Working with a blockchain is different from updating a conventional database. An application with database access can change records, but it cannot simply change a blockchain's shared state. Transactions must meet the rules checked by the network. The AI agent itself usually runs off-chain. It analyzes information and decides which action to request, while the blockchain validates submitted transactions and executes any relevant smart-contract code. The AI model does not perform its reasoning on the blockchain. A decentralized autonomous organization (DAO) allows a group to coordinate decisions and manage shared resources through blockchain-based governance. Despite its name, a DAO does not need AI to operate. But it can delegate specific rights or tasks to an AI agent. An agent could therefore cast a vote or initiate a treasury payment if the DAO's rules permit it. This does not mean giving the agent unlimited authority. The organization can define which actions are allowed and which still require approval from its members. Smart contracts can enforce programmed limits, although they cannot determine whether the agent's judgment is sound. The agent can act autonomously within its assigned role, without taking control of the organization's governance. #AIAgents #Blockchain #DAO

ADIPEC 2026 is almost here, and the world's largest energy event is growing again. From 2–5 November at ADNEC Centre, Abu Dhabi: 2,260 exhibitors, more exhibition space and 1,800 speakers. More opportunities to connect. Join > adipec.com @ADIPECOfficial Partner. An expanded exhibition does more than welcome more visitors. It allows companies to show what they are building and gives potential customers more solutions to examine in person. For me, that is what makes a major industry event useful: a direct conversation can reveal whether a technology fits a real business need. Bigger in scale, bolder in ambition, greater in impact. A bigger, bolder, better ADIPEC experience. #ADIPEC2026 #ADIPEC #Energy

A technology project can succeed technically and still deliver little value if the way people work does not change. I believe organizations should plan those changes as part of the investment, not after the technology has been deployed.

AI can warn a maintenance team that a machine may fail, but McKinsey argues that the opportunity lies in what the organization does next. A prediction is useful when it changes how maintenance is planned and carried out. That requires more than a model. Microblog by @antgrasso Suppose a warning arrives days before a possible breakdown. The repair still waits because the schedule has not changed or the required part is unavailable. The model has supplied information that the process cannot yet use. Who can act on that warning while there is still time? McKinsey estimates potential cost reductions and additional uptime across asset-heavy industries. Its analysis emphasizes redesigning the maintenance process so that insights can influence decisions and lead to timely interventions. I would put maintenance teams at the center of that redesign, giving them access to information while they are planning work and enough authority to adjust priorities. #IndustrialAI #Maintenance

Finance puts AI agents to a serious test: accuracy, controls and clear audit trails. Hyphenate (@HyphenateAI) raised $45M in Series A to expand its autonomous finance platform for accounting, revenue and treasury workflows. More: bit.ly/4BqqII9 #hyphenatepartner

A familiar name in your inbox can feel reassuring. But is it really who you think it is? Attackers exploit trusted identities and everyday workflows. Making verification a habit is an important way to protect ourselves. @MSFTIssues link bit.ly/4i7ihdb v @antgrasso

Keeping up with the market means knowing which AI platforms work best for different tasks and training our teams to use them effectively. Source @VisualCap via @antgrasso

If you use AI, do not stop using your brain. An AI suggestion is one possible option, not a decision already made for you An answer may sound convincing and still leave something out. Give it the same attention you would give any advice affecting your work Microblog @antgrasso Before accepting a recommendation, check what it is based on. Does the system have enough information about your situation? Would you accept the same advice from a colleague without asking a question? Explore another option, even when the first suggestion seems reasonable. You can ask AI to propose alternatives, then compare them with what you know. If the original recommendation still makes sense, follow it. There is no need to disagree with AI just to prove you are in control. But keep the possibility of changing your mind. When new information arrives, reconsider the decision. What would make you choose differently today? AI can suggest a direction. You should remain free to take another one. #HumanCenteredAI #AIDecisionMaking