AI Governance

Congress now has a lobbyist that never registers, never discloses a client, and never leaves a paper trail, and it sits open in a browser tab on nearly every staffer's desk. The House Office of Legislative Counsel, the institution responsible for turning policy ideas into legally binding text, has reportedly been inundated with AI-generated bill proposals so riddled with incorrect statutory references and sloppy drafting that its lawyers now spend more time fixing them than they would spend writing the bills from scratch. Around the same time, it became clear how far this had spread. Members of Congress and their staff are increasingly using systems such as ChatGPT, Claude, Copilot, and Gemini for policy research, legislative drafting, speeches, and constituent replies.   On the surface, this looks like any workplace chasing efficiency. But Congress is not any workplace. The information lawmakers receive, the way problems get framed, and the exact language that ends up in statute can affect millions of people. So the real question is not whether chatbots make congressional offices faster. It is what happens once the systems helping policymakers research, frame, and write legislation become an active participant in the political process itself.

Cybersecurity has always favoured the attacker, but AI is widening that advantage. By compressing complex operations into automated decision loops, autonomous agents let a small human team delegate the bulk of an attack's tactical work, reconnaissance, exploitation, lateral movement, to machine tempo, in at least one documented case shifting 80–90% of that labour onto the AI itself. That doesn’t make attacks free, but it does mean cost scales far more slowly with the number of targets than it used to. Enterprises, meanwhile, face the opposite trend: breach costs are climbing, and a new governance-driven Shadow AI tax is compounding them. As automation lowers the labour cost of offense while recovery expenses and regulatory demands rise on defence, the balance of cybersecurity economics is being forced to overhaul itself.

Smart glasses are no longer an experimental technology waiting to prove whether consumers will adopt them. They are already spreading rapidly, with Meta having sold more than 7 million pairs of its Ray-Ban smart glasses and daily use reportedly tripling year over year in early 2026. At the same time, the technology is becoming more capable, combining cameras, microphones, AI, translation, navigation, audio, and visual recognition in an object designed to remain on the user's face throughout the day. As manufacturers expand the technology and the market continues to grow, restaurants, theatres, workplaces, conferences, and other venues are increasingly questioning whether existing rules governing photography and recording are adequate.   The question, therefore, is no longer whether smart glasses will become part of everyday life, but what their expanding adoption will do to privacy and public space. Unlike smartphones, which normally require a visible gesture to record, smart glasses can capture and interpret their surroundings while their wearer continues to behave normally. As adoption expands, surveillance power is consequently shifting beyond governments, companies, and security systems towards ordinary individuals. This raises broader questions about who controls the information collected, who owns it, how it can be used after capture, and whether privately generated recordings could become another source of institutional surveillance.