Anthropic, the artificial intelligence (AI) company known for creating the chatbot Claude, recorded a net loss of more than $80bn in the first half of 2026, according to the Wall Street Journal, as the market anticipates its looming initial public offering (IPO). The corporation also indicated it could spend up to $750bn in coming years to acquire more computing capacity, including potential deals currently under discussion, the Journal said on Tuesday. The loss figure was up from $13bn a year earlier, according to a draft prospectus, a legal document corporations submit to financial regulators, for its IPO. The figures show the hefty costs of the AI race, even as investors look more closely at whether the money companies are pouring into chips and data centres will pay off. Anthropic also shared a list of potential risk factors, including uncertainty over how to prevent its products from doing harm, whether through sophisticated cyberattacks or blackmail by uncontrolled models.

OpenAI is aiming to list on the stock market at a valuation of about $1.1tn. OpenAI is aiming to list on the stock market at a valuation of about $1.1tn. OpenAI's London staff receive average of £2.3m per person annually OpenAI's London staff each received £2,318,181 on average last year, as the ChatGPT-maker poured Premier League football salaries into its bid to dominate the global AI race. Accounts for OpenAI UK Ltd, which is based in Westminster but is opening a new HQ in London's King's Cross area, showed that 110 staff shared £87m in salaries and £168m in shares in the company, which is aiming to list on the stock market at a valuation of about $1tn. An OpenAI worker on an average package could expect to take home about £1,000 an hour in salary and equity before tax.

OpenAI and Anthropic lost billions in predicted value after being shown up by a Chinese startup. OpenAI and Anthropic lost billions in predicted value after being shown up by a Chinese startup. Why are OpenAI and Anthropic cheering on regulation in Australia? The companies hope to follow in the footsteps of SpaceX, which raised $86bn and soared to a $2.1tn valuation after it listed on public markets in June Top US AI developers Anthropic and OpenAI cheered when Australia announced it would set new AI rules. Big tech celebrating limits on their Silicon Valley VC-funded free-for-all might seem counterintuitive but there's a much broader play than just what happens in one relatively small market.

Modular, one of the most promising chip software startups of the AI era, heads for a multibillion-dollar exit. Qualcomm will acquire the Silicon Valley chip startup Modular for nearly $4 billion. The companies announced the acquisition on Wednesday; Qualcomm said it expects to issue up to 19.2 million shares of common stock in the deal, which works out to just under $4 billion based on the company's last closing share price. The deal, which includes $300 million for Modular employees, comes nine months after the chip startup raised $250 million at a $1.6 billion valuation . It's expected to close in the second half of this year.

Data Shapley is an important tool for data valuation, which quantifies the contribution of individual data points to machine learning models. In practice, group-level data valuation is desirable when data providers contribute data in batch. However, we identify that existing group-level extensions of Data Shapley are vulnerable to shell company attacks, where strategic group splitting can unfairly inflate valuations. We propose Faithful Group Shapley Value (FGSV) that uniquely defends against such attacks. Building on original mathematical insights, we develop a provably fast and accurate approximation algorithm for computing FGSV. Empirical experiments demonstrate that our algorithm significantly outperforms state-of-the-art methods in computational efficiency and approximation accuracy, while ensuring faithful group-level valuation.

We study revenue maximization for agents with additive preferences, subject to downward-closed constraints on the set of feasible allocations. In seminal work, Alaei [Ala14] introduced a powerful multi-to-single agent reduction based on an ex-ante relaxation of the multi-agent problem. This reduction employs a rounding procedure which is an online contention resolution scheme (OCRS) in disguise, a now widely-used method for rounding fractional solutions in online Bayesian and stochastic optimization problems. In this paper, we leverage our vantage point, 10 years after the work of Alaei, with a rich OCRS toolkit and modern approaches to analyzing multi-agent mechanisms; we introduce a general framework for designing non-sequential and sequential multi-agent, revenue-maximizing mechanisms, capturing a wide variety of problems Alaei's framework could not address. Our framework uses an interim relaxation, that is rounded to a feasible mechanism using what we call a two-level OCRS, which allows for some structured dependence between the activation of its input elements. For a wide family of constraints, we can construct such schemes using existing OCRSs as a black box; for other constraints, such as knapsack, we construct such schemes from scratch. We demonstrate numerous applications of our framework, including a sequential mechanism that guarantees a 2ee 1 3.16 approximation to the optimal revenue for the case of additive agents subject to matroid feasibility constraints. The simplicity of our developed two-level CRSs and OCRSs highlights the strength of our framework: even with a simple analysis, it yields state-of-the-art approximation guarantees across a wide range of settings. Finally, we show how it naturally extends to multi-parameter procurement auctions.

Meritocratic systems, from admissions to hiring, aim to impartially reward skill and effort.

Data valuation techniques quantify each training example's contribution to model performance, providing a principled basis for data cleaning, acquisition, and selection. Existing valuation methods remain inadequate: model-based techniques depend on a single fitted model and inherit its biases, while algorithm-based approaches like Data Shapley scale poorly due to their need to train multiple models. Recent work has proposed model-agnostic alternatives based on Wasserstein distance between the training set and a clean reference set, but exact computation is expensive and approximations often misrank examples. We introduce KAIROS, a model-agnostic framework that values examples by their contribution to the Maximum Mean Discrepancy (MMD) between the training set and a clean reference distribution. Unlike Wasserstein methods, MMD admits a closed-form solution that requires no approximations and is scalable to large datasets. Additionally, KAIROS enables efficient online valuation: adding a new batch of m examples requires only O(mN)computation to update all scores, compared to O(N2)in prior work where N is the training set size. Empirical evaluations on noise, mislabeling, and poisoning benchmarks show that KAIROS consistently outperforms state-of-the-art baselines in both accuracy and runtime. On ImageNet, KAIROS achieves up to 15 speedup over the fastest baseline while maintaining superior data valuation quality. Our results demonstrate that model-agnostic methods can match or exceed model-based approaches in performance while scaling to large datasets.

Data Shapley values provide a principled approach for quantifying the contribution of individual training examples to machine learning models. However, computing these values often requires computational complexity that is exponential in the data size, and this has led researchers to pursue efficient algorithms tailored to specific machine learning models. Building on the prior success of the Shapley valuation for K-nearest neighbor (KNN) models, in this paper, we introduce a localized data Shapley framework that significantly accelerates the valuation of data points.

We study the online multi-class selection problem with group fairness guarantees, where limited resources must be allocated to sequentially arriving agents.