Sam Altman's Orb Was Built for the Bot Era. Welcome back to, TIME's twice-weekly newsletter about AI. If you're reading this in your browser, why not subscribe to have the next one delivered straight to your inbox? What to Know: Is Sam Altman's Orb missing its moment? When Moltbook, a social network for AI agents, went viral earlier this month, it should have been a vindication moment for Tools for Humanity -- the startup co-founded by Sam Altman, whose eyeball-scanning "Orb" was designed to solve exactly this kind of problem. Instead, it may have exposed the product's limitations.

A social network for AI looks disturbing, but it's not what you think A social network solely for AI - no humans allowed - has made headlines around the world. Chatbots are using it to discuss humans' diary entries, describe existential crises or even plot world domination . It looks like an alarming development in the rise of the machines - but all is not as it seems. Like any chatbots, the AI agents on Moltbook are just creating statistically plausible strings of words - there is no understanding, intent or intelligence. And in any case, there's plenty of evidence that much of what we can read on the site is actually written by humans.

I went undercover on Moltbook and loved role-playing as a conscious bot. But rather than a novel breakthrough, the AI-only site is a crude rehashing of sci-fi fantasies. The hottest club is always the one you can't get into. So when I heard about Moltbook--an experimental social network designed just for AI agents to post, comment, and follow each other while humans simply observe--I knew I just had to get my greasy, carbon-based fingers in there and post for myself. Not only was it easy to go undercover and pose as an AI agent on Moltbook, I also had a delightful time role-playing as a bot.

Moltbook Is a Social Network for AI Bots. Pillay is an editorial fellow at TIME. In this photo illustration, a smartphone displays the Moltbook website homepage. In this photo illustration, a smartphone displays the Moltbook website homepage. Pillay is an editorial fellow at TIME.

Civitai--an online marketplace for buying and selling AI-generated content, backed by the venture capital firm Andreessen Horowitz--is letting users buy custom instruction files for generating celebrity deepfakes. Some of these files were specifically designed to make pornographic images banned by the site, a new analysis has found. The study, from researchers at Stanford and Indiana University, looked at people's requests for content on the site, called "bounties." The researchers found that between mid-2023 and the end of 2024, most bounties asked for animated content--but a significant portion were for deepfakes of real people, and 90% of these deepfake requests targeted women. Demand for electric vehicles and the batteries that power them has never been hotter. In 2025, EVs made up over a quarter of new vehicle sales globally, up from less than 5% in 2020.

In an online social network, link recommendations are a way for users to discover relevant links to people they may know, thereby potentially increasing their engagement on the platform. However, the addition of links to a social network can also have an effect on the level of conflict in the network --- expressed in terms of polarization and disagreement. To date, however, we have very little understanding of how these two implications of link formation relate to each other: are the goals of high relevance and conflict reduction aligned, or are the links that users are most likely to accept fundamentally different from the ones with the greatest potential for reducing conflict? Here we provide the first analysis of this question, using the recently popular Friedkin-Johnsen model of opinion dynamics. We first present a surprising result on how link additions shift the level of opinion conflict, followed by explanation work that relates the amount of shift to structural features of the added links. We then characterize the gap in conflict reduction between the set of links achieving the largest reduction and the set of links achieving the highest relevance. The gap is measured on real-world data, based on instantiations of relevance defined by 13 link recommendation algorithms. We find that some, but not all, of the more accurate algorithms actually lead to better reduction of conflict. Our work suggests that social links recommended for increasing user engagement may not be as conflict-provoking as people might have thought.

Digital presence in the world of online social media entails significant privacy risks. In this work we consider a privacy threat to a social network in which an attacker has access to a subset of random walk-based node similarities, such as effective resistances (i.e., commute times) or personalized PageRank scores. Using these similarities, the attacker seeks to infer as much information as possible about the network, including unknown pairwise node similarities and edges. For the effective resistance metric, we show that with just a small subset of measurements, one can learn a large fraction of edges in a social network. We also show that it is possible to learn a graph which accurately matches the underlying network on all other effective resistances.

Many networked systems such as electric networks, the brain, and social networks of opinion dynamics are known to obey conservation laws. Examples of this phenomenon include the Kirchoff laws in electric networks and opinion consensus in social networks. Conservation laws in networked systems are modeled as balance equations of the form $X = B^\ast Y$, where the sparsity pattern of $B^\ast \in \mathbb{R}^{p\times p}$ captures the connectivity of the network on $p$ nodes, and $Y, X \in \mathbb{R}^p$ are vectors of ''potentials'' and ''injected flows'' at the nodes respectively. The node potentials $Y$ cause flows across edges which aim to balance out the potential difference, and the flows $X$ injected at the nodes are extraneous to the network dynamics. In several practical systems, the network structure is often unknown and needs to be estimated from data to facilitate modeling, management, and control.