India's Prime Minister Narendra Modi takes a group photo with AI company leaders at the AI Impact Summit in New Delhi on Feb. 19, 2026. India's Prime Minister Narendra Modi takes a group photo with AI company leaders at the AI Impact Summit in New Delhi on Feb. 19, 2026. The world's largest-ever AI summit took place in India this week, with hundreds of thousands of people, including world leaders and CEOs of AI companies, descending upon New Delhi for five days. It was the fourth in a series of summits that were initially designed as a place for governments to coordinate global action in the face of threats from advanced AI. But the India summit, like one in Paris before it, functioned more as a trade fair and an advertisement for the host nation's AI prowess than a venue for meaningful international diplomacy.

Indian Prime Minister Narendra Modi takes a group photo with leaders of artificial intelligence companies at the AI Impact Summit in New Delhi on Thursday. But analysts said the country was unlikely to have a "DeepSeek moment" -- the sort of boom China had last year with a high-performance, low-cost chatbot -- any time soon. Still, building custom AI tools could bring benefits to the world's most populous nation. In a time of both misinformation and too much information, quality journalism is more crucial than ever. By subscribing, you can help us get the story right. With your current subscription plan you can comment on stories.

The proliferation of Large Language Models (LLMs) necessitates efficient mechanisms to distinguish machine-generated content from human text. While statistical watermarking has emerged as a promising solution, existing methods suffer from two critical limitations: the lack of a principled approach for selecting sampling distributions and the reliance on fixed-horizon hypothesis testing, which precludes valid early stopping. In this paper, we bridge this gap by developing the first e-value-based watermarking framework, Anchored E-Watermarking, that unifies optimal sampling with anytime-valid inference. Unlike traditional approaches where optional stopping invalidates Type-I error guarantees, our framework enables valid, anytime-inference by constructing a test supermartingale for the detection process. By leveraging an anchor distribution to approximate the target model, we characterize the optimal e-value with respect to the worst-case log-growth rate and derive the optimal expected stopping time. Our theoretical claims are substantiated by simulations and evaluations on established benchmarks, showing that our framework can significantly enhance sample efficiency, reducing the average token budget required for detection by 13-15% relative to state-of-the-art baselines.

Reasoning with LLMs increasingly unfolds inside a broader verification loop. Internally, systems use cheap checks, such as self-consistency or proxy rewards, which we call weak verification. Externally, users inspect outputs and steer the model through feedback until results are trustworthy, which we call strong verification. These signals differ sharply in cost and reliability: strong verification can establish trust but is resource-intensive, while weak verification is fast and scalable but noisy and imperfect. We formalize this tension through weak--strong verification policies, which decide when to accept or reject based on weak verification and when to defer to strong verification. We introduce metrics capturing incorrect acceptance, incorrect rejection, and strong-verification frequency. Over population, we show that optimal policies admit a two-threshold structure and that calibration and sharpness govern the value of weak verifiers. Building on this, we develop an online algorithm that provably controls acceptance and rejection errors without assumptions on the query stream, the language model, or the weak verifier.

A new artificial intelligence (AI) model developed by the Chinese company behind TikTok rocked Hollywood this week - not just because of what it can do, but what it could mean for creative industries. Created by tech giant ByteDance, Seedance 2.0 can generate cinema-quality video, complete with sound effects and dialogue, from just a few written prompts. Many of the clips said to have been made using Seedance, and featuring popular characters like Spider-Man and Deadpool, went viral. What is Seedance - and why the stir? Seedance was launched to little fanfare in June 2025 but it is the second version that came eight months later that has caused a major stir.

Workers describe a deteriorating culture at Block, the company behind Square and Cash App, where layoffs continue and employees are expected to use AI tools daily. After hundreds of workers were laid off in early February from Jack Dorsey's Block, some of the people remaining at the company say the internal culture has devolved to a point where performance anxiety is running rampant, using generative AI is required, and overall morale is rapidly deteriorating. Block is the parent company behind the merchant payment processor Square and the payment app Cash App. "Morale is probably the worst I've felt in four years," reads an employee complaint submitted to Dorsey in a recent all-hands meeting, a transcript of which was seen by WIRED. "The overarching culture at Block is crumbling."

The Boston startup uses AI to translate and verify legacy software for defense contractors, arguing modernization can't come at the cost of new bugs. Code Metal, a Boston-based startup that uses AI to write code and translate it into other programming languages, just closed a $125 million Series B funding round from new and existing investors. The news comes just a few months after the startup raised $36 million in series A financing led by Accel. Code Metal is part of a new wave of startups aiming to modernize the tech industry by using AI to generate code and translate it across programming languages. One of the questions that persists about AI-assisted code, though, is whether the output is any good--and what the consequences might be if it's not.

We introduce Dataset Grouper, a library to create large-scale group-structured (e.g., federated) datasets, enabling federated learning simulation at the scale of foundation models. This library facilitates the creation of group-structured versions of existing datasets based on user-specified partitions, and directly leads to a variety of useful heterogeneous datasets that can be plugged into existing software frameworks. Dataset Grouper offers three key advantages. First, it scales to settings where even a single group's dataset is too large to fit in memory. Second, it provides flexibility, both in choosing the base (non-partitioned) dataset and in defining partitions.

The AI search startup once predicted advertising would be a massive business. Perplexity is abandoning plans to put ads in its AI search product as the industry looks for sustainable business models that won't hurt user trust. The changes are part of a larger strategic shift for the company, which has long focused on disrupting Google Search's business. Google is changing to be like Perplexity more than Perplexity is trying to take on Google, said a Perplexity executive at a press briefing on Tuesday. Executives spoke to the press on the condition of anonymity.