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10/17/2025: Dispatch from World Summit AI

Photo Credit: Margo Gustia

World AI Week was in Amsterdam (NL) last week (October 6-10), with the World Summit AI as its marquee event. For two days, thousands of attendees listened to and engaged discussions of AI impacts across social dimensions like workforce, education, civic structure, and individual relationships. With regular nods to the very real concerns about the current corporate AI oligopoly, the overall tone for the week was that of an excited pep rally for an AI saturated future.

A recurring theme among engineering presenters and firm exhibitors is that 2025 is the year of AI Agents. In Google’s free and self-congratulatory publication Forward (given to every attendee), the Public Worldwide Examples piece was really a collection of AI agent use-cases. Most of the exhibitors and many of the technical presenters (in the Tech Deep Dive track) were there with agentic engineering innovations, code-sharing mechanisms, out-of-the-box agent development platforms, and personal data security agents. While a broader complaint about AI is that no one needs generative AI, use-specific AI agents is the innovation that public and private sectors are demanding. Most startling for me, as a new-to-AI librarian, was how “easy” developing effective custom solutions has become.

While software development innovations abounded, the robotics dimension of AI, where hardware and software need to be co-developed and deployed, is moving more slowly. Surprisingly, the constraint in the intelligent autonomous mechanical beings sector is data availability. AI models are mostly some kind of language model trained on text (and text descriptions of images). In robotics, innovations in data types and modeled understanding of video data types are needed. According to Jennifer Ding (ML Solutions Engineer at Encord), if a robot is being designed to wash dishes a video dataset of dish washing is needed. Encord and other firms help develop those data by hiring human actors to do the activity they wish to train robots to do, filmed from a variety of angles with detail shots of movement physicality.

In the same robotics panel discussion, Limor Schweitzer (founder, MOV.AI) noted that the demand for robotics was a demand for unpaid labor. While there was an arguable use-case for hazardous employment that makes pro-human sense, like oil rig and underwater mechanics work, all panelists agreed that home health care companies were the most eager for their products to be ready for deployment. Multiple sessions were run by pro-efficiency industrialists who talked about AI deployments as helping them move to “dark factories”, places where only automatons operated. Although these presenters took the time to talk about how this meant that their human workforce was given more time to interact with human customers, where the goal is maximum profit it’s easy to see a future with no human interaction at all. As Sharon Sochil Washington (founder, MASi) noted, as language model trained interfaces become primary education and medical interactions for people worldwide, the base data used to train the AI, will mean hundreds and hundreds of cultures won’t be represented in that future.

Karen Hao (author, Empire of AI) gave the opening keynote for World Summit AI. She exhorted the audience to not see the global deployment of US owned AI systems as the “democratizing”. She used a term she borrowed, techno-authoritarianism, to describe this era of minimally regulated AI spread. In properly naming the current landscape of expansionist exploitation by the big AI tech companies, countries and municipalities stand a better chance of effectively developing and implementing local governance solutions that work for their people. In my opinion, hers was the most optimistic talk of the week.