On today's, we dive into the dysfunction in Meta's newly formed AI unit and why it's been driving already-low employee morale even further into the ground. This week on, our hosts discuss the meltdown that has been recently unfolding at Meta and what it says about the company's relentless ambitions in the AI race. They also dive into the leaked messages and names of an invite-only group cofounded by billionaire tech founder Peter Thiel, and how Sam Bankman-Fried is now actively seeking a pardon from the Trump administration. Plus, they share their impressions on SpaceX acquiring Cursor and the latest on the negotiations between Anthropic and the government. 'Tell Him He's a Piece of Shit': Meta's New AI Unit Is a Total Mess Write to us at [email protected] . You can always listen to this week's podcast through the audio player on this page, but if you want to subscribe for free to get every episode, here's how: If you're on an iPhone or iPad, open the app called Podcasts, or just tap this link . Before we start, two quick things. If you've been enjoying listening to the show, we would appreciate it if you took a second to rate it in your podcast app of choice. It really helps us reach more people. And second, if you have any questions related to tech, privacy, or politics that you would like me, Zoë, and Leah to take on, now is the time to submit them to [email protected] . It doesn't matter how big or how small, we want to hear from you and get you answers. Today on the show, we're talking about the dysfunction in Meta's newly formed AI unit and why it's been driving employee morale, which was already very, very low, even further into the ground. We'll also break down the recent online leak that shed light on Peter Thiel's invite-only group, Dialog, more than 200 names of high profile people in government, tech, academia, beyond are listed in the documents as members and guests of this secretive society, not to mention a look at what they talk about behind closed doors.

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Large language models (LLMs) are increasingly being asked to make economically rational decisions and indeed are already being applied to economic tasks like stock picking and financial analysis. Existing LLM benchmarks tend to focus on specific applications, making them insufficient for characterizing economic reasoning more broadly. In previous work, we offered a blueprint for comprehensively benchmarking strategic decision-making Raman et al. [2024]. However, this work did not engage with the even larger microeconomic literature on non-strategic settings. We address this gap here, taxonomizing microeconomic reasoning into 58distinct elements, each grounded in up to 10distinct domains, 5perspectives, and 3types. The generation of benchmark data across this combinatorial space is powered by a novel LLM-assisted data generation protocol that we dub auto-STEER, which generates a set of questions by adapting handwritten templates to target new domains and perspectives. By generating fresh questions for each element, auto-STEER induces diversity which could help to reduce the risk of data contamination. We use this benchmark to evaluate 27LLMs spanning a range of scales and adaptation strategies, comparing performance across multiple formats--multiple-choice and free-text question answering--and scoring schemes. Our results surface systematic limitations in current LLMs' ability to generalize economic reasoning across types, formats, and textual perturbations, and establish a foundation for evaluating and improving economic competence in foundation models.

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'This is an apartheid regime' Does Trump have real leverage over Netanyahu? At least three Palestinians have been killed and several others wounded after an Israeli drone struck a vehicle near Abu Khadra Mosque in the Rimal neighbourhood of western Gaza City, according to medical sources. Al Jazeera's Hind Khoudary, reporting from Gaza City, said the attack on Thursday was the first explosion in the area after a few "calm and quiet" days. What to know about Colombia's run-off election "Only one of the three victims has been identified: Abdul Jawad Abu Lebn [who] was set to get married next week. Wedding invitations were found inside the car."

Automated interpretability research aims to identify concepts encoded in neural network features to enhance human understanding of model behavior. Within the context of large language models (LLMs) for natural language processing (NLP), current automated neuron-level feature description methods face two key challenges: limited robustness and the assumption that each neuron encodes a single concept (monosemanticity), despite increasing evidence of polysemanticity. This assumption restricts the expressiveness of feature descriptions and limits their ability to capture the full range of behaviors encoded in model internals. To address this, we introduce Polysemantic FeatuRe Identification and Scoring Method (PRISM), a novel framework specifically designed to capture the complexity of features in LLMs. Unlike approaches that assign a single description per neuron, common in many automated interpretability methods in NLP, PRISM produces more nuanced descriptions that account for both monosemantic and polysemantic behavior. We apply PRISM to LLMs and, through extensive benchmarking against existing methods, demonstrate that our approach produces more accurate and faithful feature descriptions, improving both overall description quality (via a description score) and the ability to capture distinct concepts when polysemanticity is present (via a polysemanticity score).

The software engineers filed a complaint with Seattle's civil rights office accusing Amazon of illegally retaliating against them for expressing their personal political beliefs. Earlier this month, five current Amazon employees publicly urged Seattle City Council to regulate data centers . It was an unprecedented act of advocacy by tech workers, and now three of the staffers say they are under internal investigation for what they understand to be allegedly representing themselves as spokespeople for the company without prior approval. "It's a totally ridiculous claim," says one of the affected employees, Patrick Schloesser. The three software engineers, who work in different divisions of Amazon and all live in Seattle, believe they are being unfairly targeted for expressing their political beliefs.

Existing safety interventions - ranging from training data curation and model fine-tuning to inference-time filtering and guidance - often suffer from incomplete concept removal, susceptibility to jailbreaking, computational inefficiency, or collateral damage to unrelated capabilities. In this paper, we introduce CURE, a training-free concept unlearning framework that operates directly in the weight space of pre-trained diffusion models, enabling fast, interpretable, and highly specific suppression of undesired concepts. At the core of our method is the Spectral Eraser, a closed-form, orthogonal projection module that identifies discriminative subspaces using Singular Value Decomposition over token embeddings associated with the concepts to forget and retain. Intuitively, the Spectral Eraser identifies and isolates features unique to the undesired concept while preserving safe attributes. This operator is then applied in a single step update to yield an edited model in which the target concept is effectively 39th Conference on Neural Information Processing Systems (NeurIPS 2025).

Extensive experimental results demonstrate that GeneMAN could generate high-quality 3D human models from a single image input, outperforming prior state-of-the-art methods. Notably, GeneMAN could reveal much better generalizability in dealing with in-the-wild images, often yielding high-quality 3D human models in natural poses with common items, regardless of the body proportions in the input images.