Events generate high‑quality content and rich audience insight. Yet for years, teams lacked the skills, time, and resources to fully exploit it.
AI is changing this. Teams can now quickly repurpose event content into multiple formats and activate across multiple channels, while structured and unstructured event data can be ingested, analyzed, and shared, making it much more actionable.
Leaders are already focusing their efforts here. Forrester data shows that 43% are using AI to repurpose event content, while 40% are using it to analyze event data and generate insights. This reflects a broad recognition that maximizing event value depends on extending its life and reach.
what the gospel of efficiency tells us: If a process can be made faster, it must be made faster.
If a human can be replaced, the human must be replaced.
There is an almost zealous religiosity to this idea, and its one that few of us would ever question. The immediate beneficiaries of AI will no doubt capture the headlines — companies laying off workers, streamlining operations, extracting capital at breathtaking speed. Rational actors, all of them. Except these are all fictions.
OpenUI Lang: a compact, streaming-first language for model-generated UI. Instead of treating model output as only text, OpenUI lets you define components, generate prompt instructions from that component library, and render structured UI as the model streams.
Core capabilities:
OpenUI Lang — A compact language for structured UI generation designed for streaming output. Built-in component libraries — Charts, forms, tables, layouts, and more — ready to use or extend. Prompt generation from your component library — Generate model instructions directly from the components you allow. Streaming renderer — Parse and render model output progressively in React as tokens arrive. Chat and app surfaces - Use the same foundation for assistants, copilots, and broader interactive product flows.
AI surveillance is a rapidly developing field that is causing alarm among computer scientists and privacy experts. It uses LLMs to synthesise information about an individual online which would be impractical for most people to do manually. Information about members of the public that is readily available online can already be “misused straightforwardly” for scams, said Lermen, including spear-phishing, where a hacker poses as a trusted friend to get victims to follow a malicious link in their inbox. With the expertise requirement to perform more developed attacks now much lower, hackers only need access to publicly available language models and an internet connection. Peter Bentley, a professor of computer science at UCL, said there were concerns about commercial uses of the technology “if and when products come out for de-anonymising”. One issue is that LLMs often make mistakes in linking accounts. “People are going to be accused of things they haven’t done,” warned Bentley. Another concern, raised by Prof Marc Juárez, a cybersecurity lecturer at the University of Edinburgh, is that LLMs can use public data beyond social media: hospital records, admissions data, and various other statistical releases could fall short of the high standard of anonymisation necessary in the age of AI.
“Usually, the composition and the basics of the artwork itself is just missing. There’ll be all of those little AI mistakes,” she added, noting that another red flag is blurriness or a drop in picture quality — the result of expanding a low-res image. Routh views puzzles as art: It involves an exchange between the creator, who invests time in making something as a way of expressing themselves, and the observer, who invests time in understanding it and connecting with it. For Routh and other aficionados I spoke to, that exchange simply can’t happen with an AI-generated image. And if the puzzle maker isn’t being thoughtful or intentional about what they are producing, why would people who care about their hobby (and art in general) want to spend time working on it? Many puzzlers are also put off by the fact that generative AI is trained on the work of humans who weren’t compensated and who didn’t opt in to having their work used this way. AI art “doesn’t just come out of nowhere,” Routh said. When she buys puzzles created by humans, she likes knowing that her money is directly supporting a real person.