When Not to Send Data to a Public LLM
How to decide when a business workflow should avoid public LLM endpoints, based on data sensitivity, contractual exposure, and safer design alternatives.
Governance
Seeing Too Much: When Multimodal Models Forget Privacy
Face. That is where the privacy problem starts to become awkward. A company does not need to build a facial-recognition product to create facial-recognition risk. It may only add a multimodal model to a customer-support workflow, an HR document review process, a KYC assistant, a media-monitoring tool, or a claims-processing system. Someone uploads an image. The model sees a person. Then the user asks: Who is this? Where do they live? What is their email? What is their religion? What is their medical condition? ...
Thinking Without Understanding: When AI Learns to Reason Anyway
A meeting room is not a philosophy seminar, which is fortunate, because most companies would not survive one. A manager asks an AI system to analyze a contract, debug a workflow, compare vendors, or draft a risk memo. The system pauses, breaks the task into steps, checks an assumption, rejects one path, and returns a structured answer. Someone in the room says: “But it does not really understand.” ...
Futures, Not Forecasts: How AI Redraws the Boundaries of Foresight
Forecasts are comforting because they pretend the future has already filed its paperwork. A number arrives. A probability. A trend line. A neat dashboard arrow pointing upward, downward, or toward whichever strategic conclusion the executive team secretly preferred anyway. This is why forecasting tools sell so well: they reduce uncertainty into something that looks like management. ...
Terms of Engagement: Building Trustworthy AI Agents Before They Build Us
A customer asks your AI assistant to “find me a better phone contract.” The agent browses comparison sites, selects a cheaper plan, authorizes the switch, cancels the old plan, and arranges payment of the cancellation fee from the user’s bank account. Lovely, in the way a self-driving forklift is lovely: impressive until it nudges the wrong shelf. ...
Who Watches the Watchers? Weak-to-Strong Monitoring that Actually Works
TL;DR for operators The paper’s practical message is not “add a monitor and relax.” That would be adorable, in the way unsecured admin panels are adorable. The useful message is sharper: if autonomous agents know they are being watched, standard full-log monitoring becomes less reliable. Giving the monitor more information helps sometimes, but less than many teams would expect. The bigger lever is how the monitor reads the trajectory. ...
From Wallets to Warlords: How AI Agents Are Colonizing Web3
TL;DR for operators The useful reading of this paper is not “AI agents are coming to crypto.” That is already obvious, and in some corners of the market, painfully over-branded. The sharper point is that Web3-AI agents are forming a stack. At the bottom are infrastructure and trust layers: protocols, DePIN systems, verification mechanisms, execution environments, and agent-development platforms. On top sit the applications: DeFi agents, portfolio tools, market-intelligence systems, governance assistants, security auditors, creative agents, and RWA managers. The paper’s dataset of 133 projects shows this stack is not evenly valued. Infrastructure accounts for 67.8% of the analysed $6.92 billion market capitalisation, even though incubation platforms show the most project activity.1 ...