Fri 24 April 2026
Daily Brief — Curated and contextualised by Best Practice AI
Meta Cuts Jobs, Intel Shares Soar, and China Clamps Down
TL;DR Meta will cut 10% of its workforce to fund a $135bn investment in data centers. Intel's shares surged 20% as AI-driven demand boosted revenue to $13.6 billion. China plans to restrict US investments in its tech sector following Meta's acquisition of Manus. Cohere and Aleph Alpha announced a $20bn partnership to develop sovereign AI systems independent of US and China influence.
The stories that matter most
Selected and contextualised by the Best Practice AI team
Cohere and Aleph Alpha agree $20bn transatlantic AI tie-up
Canadian and German start-ups to focus on ‘sovereign’ AI systems independent of US and China
Ideological Bias in LLMs' Economic Causal Reasoning
arXiv:2604.21334v1 Announce Type: cross Abstract: Do large language models (LLMs) exhibit systematic ideological bias when reasoning about economic causal effects? As LLMs are increasingly used in policy analysis and economic reporting, where directionally correct causal judgments are essential, this question has direct practical stakes. We present a systematic evaluation by extending the EconCau
Preserving Decision Sovereignty in Military AI: A Trade-Secret-Safe Architectural Framework for Model Replaceability, Human Authority, and State Control
arXiv:2604.20867v1 Announce Type: new Abstract: Recent events surrounding the relationship between frontier AI suppliers and national-security customers have made a structural problem newly visible: once a privately governed model becomes embedded in military workflows, the supplier can influence not only technical performance but also the operational boundary conditions under which the system ma
DeepSeek unveils new AI model tailored for Huawei chips as China pushes for tech autonomy | Reuters
Most leading AI models are trained and run on chips made by Nvidia. And DeepSeek's pivot to Huawei underscores concerns raised by Nvidia CEO Jensen Huang that the U.S. firm risks losing its developer ecosystem in China due to U.S.
AI Is Forcing IT Services to Rethink How They Get Paid - Newsweek
AI is reshaping IT services pricing. “The model no longer reflects how value is created,” Abby Kearns told Newsweek.
Talking to AI agents is one thing — what about when they talk to each other? New startup BAND debuts 'universal orchestrator'
For the past eighteen months, the corporate world has been obsessed with the "builder" phase of the generative AI revolution. Enterprises have raced to deploy autonomous agents to handle everything from customer support to complex codebase refactoring. However, as these digital workers proliferate, a new, more structural problem has emerged: fragmentation. Agents built on LangChain cannot easily
There are fixes for AI’s toll on the power grid. Here’s why they’re not happening | CNN Business
Tech companies charging ahead with artificial intelligence have a problem: AI’s rapid growth is colliding headlong with a finite amount of available energy and computing power.
Value-Conflict Diagnostics Reveal Widespread Alignment Faking in Language Models
arXiv:2604.20995v1 Announce Type: new Abstract: Alignment faking, where a model behaves aligned with developer policy when monitored but reverts to its own preferences when unobserved, is a concerning yet poorly understood phenomenon, in part because current diagnostic tools remain limited. Prior diagnostics rely on highly toxic and clearly harmful scenarios, causing most models to refuse immedia
Economics & Markets
BlackRock Likes Chips, Hardware in Overweight AI Stance
Wei Li, chief global investment strategist at BlackRock, discusses the outlook for artificial intelligence stocks, capital expenditure and earnings. "We're specifically overweight semis and hardware," Li tells Bloomberg Television. (Source: Bloomberg)
Inflated AI Claims Are Under Fire—and the Regulatory Reckoning Is Coming | Fortune
A top securities litigation partner at Baker McKenzie argues that history—from dot-com fraud to ESG greenwashing—tells us exactly where AI disclosure claims are headed.
TSMC Shares Surge as Taiwan Lifts Single-Stock Limit for Funds
Taiwan Semiconductor Manufacturing Co. shares climbed to a record after Taiwan’s financial regulator eased limits on single-stock fund holdings, a move that JPMorgan Chase & Co. said could draw more than $6 billion of inflows.
UBS says AI investment cycle is broadening even as market grows more selective on returns
The bank sees the next phase of the AI trade rewarding execution and returns rather than mere exposure to the theme, and retains an attractive view on US...
Siemens Energy (SIEGY) Upgrades 2026 Financial Outlook Amid AI Data Center Demand
On April 23, 2026, Siemens Energy (SIEGY) announced a revision of its financial guidance for the fiscal year 2026, reflecting a significant increase in demand f
The market's biggest semiconductor index is on its longest-ever winning streak. Here's what could trip it up.
The Philadelphia Semiconductor Index is on a record hot streak, up almost 40% in 16 days on renewed excitement for AI among investors.
Quant pioneer Martin Lueck warns against handing over trading to AI
Caution by co-founder of Aspect hedge fund follows billionaire Cliff Asness’s decision to ‘surrender’ to the machines
Google explains why its all-in-one AI stack embraces competitors
'Differentiated, but open' Google Cloud Next Google Cloud’s Andi Gutmans said that the company holds a structural advantage over its largest rivals in the race to win value from AI agents in the enterprise, arguing that no competitor currently combines cloud computing infrastructure, frontier AI models, and a data platform under one roof.…
ChatGPT Could Be Officially Labeled a Major Search Engine by the EU — And OpenAI Probably Isn’t Celebrating
OpenAI’s own transparency report, published under existing DSA obligations, revealed that ChatGPT’s search feature averaged 120.4 million monthly
Demand Curvature and Pass-Through in Differentiated Oligopoly
arXiv:2604.21423v1 Announce Type: new Abstract: This paper studies cost pass-through in differentiated-product oligopoly. I derive a general representation of the pass-through matrix that decomposes equilibrium price responses into the roles of demand curvature, substitution, and multiproduct ownership. This extends the classic insight in single-product monopoly to multiproduct settings in which diversion and ownership also matter. I then develop a tractable first-order approximation that yields a sufficient-statistics characterization for empirically relevant demand systems. Finally, I characterize the small-share limit and show how common demand specifications impose tail restrictions that shape pass-through. The results provide a practical framework for applied work on tax incidence, merger analysis, and related questions in imperfect competition.
Lightelligence Is Said Set to Price HK IPO at Top of Range
Chinese optical-computing company Lightelligence is poised to price its oversubscribed Hong Kong initial public offering at the top of the marketed range, according to people familiar with the matter, signaling hot demand for components used in the artificial-intelligence buildout.
Talking to AI agents is one thing — what about when they talk to each other? New startup BAND debuts 'universal orchestrator'
For the past eighteen months, the corporate world has been obsessed with the "builder" phase of the generative AI revolution. Enterprises have raced to deploy autonomous agents to handle everything from customer support to complex codebase refactoring. However, as these digital workers proliferate, a new, more structural problem has emerged: fragmentation. Agents built on LangChain cannot easily
The Americas’ Fastest-Growing Companies
The FT’s seventh annual ranking by compound annual revenue growth. Plus: how listed companies overcome obstacles; AI and defence start-ups dominate investment; the US and China in Latin America; and technology shakes up Canadian wealth management
Top Startup and Tech Funding News – April 23 2025 - Tech Startups
It’s Thursday, April 23, 2026, and we’re back with today’s top startup and tech funding news. Today’s rounds make one thing clear: AI is moving past experimentation and into production systems where reliability, governance, and real-world utility matter more than raw model capability.
Edtech’s pandemic boom is over as K-12 startup funding craters - Rest of World
Venture capital is moving away from K-12 edtech worldwide as investors prioritize AI tools and workforce training with clearer returns.
Exclusive: Cloneable Raises $4.6M To ‘Clone’ Expert Worker Knowledge With Agentic AI For Utilities And Infrastructure
Cloneable, a startup that uses AI to shadow human experts in heavy industries such as energy and replicate their specialized workflows into autonomous agents, has raised $4.6 million in seed funding, the company tells Crunchbase News exclusively.
UK VC term sheets highlight diverging trends between early and late-stage investment | Disruption Banking
Seed and AI rounds are largely funded by UK investors while US investors dominate late-stage deals · 23rd April, 2026 – HSBC Innovation Banking has today published its latest Venture Capital Term Sheet Guide 2026, providing insight into the evolving dynamics shaping UK startup investment.
The AI gold rush is pulling workers into startups as layoffs and RTO push them out of corporate jobs
A startup boom: AI is opening doors as layoffs and RTO push workers to go solo.
Swedish legal-tech Legora buys AI legal research start-up Qura
Qura stands out by a ‘wide margin’ in its class, Legora CEO Max Junestrand said. Read more: Swedish legal-tech Legora buys AI legal research start-up Qura
Sam Altman’s Orb Company Promoted a Bruno Mars Partnership That Doesn't Exist
Worldcoin's parent company faced scrutiny after promoting a partnership with Bruno Mars that was later revealed to be non-existent.
Labor, Society & Culture
Microsoft Targets About 7% of Its U.S. Workers With Buyout Offer
The tech giant is offering long-serving employees early retirements as it continues to invest aggressively in artificial intelligence.
What does AI really mean for your work? You asked, we answered
Sarah O’Connor, John Burn Murdoch and Madhumita Murgia replied to reader questions
The snoopy AI co-worker is coming - David Horsey's Substack
A new startup, Kuse AI , has created an AI office employee named Junior that keeps track of every human employee’s email, pace of work and progress on projects, attends every Zoom meeting and constantly reports to the boss while sending persistent notices to co-workers to pick up the pace.
HrFlow.ai secures €6 million pre-Series A to build “Hiring SuperIntelligence” to tackle unemployment
HrFlow.ai, a Paris-based startup building the data and AI infrastructure for the labour market, has raised $6 million ($7 million) in a pre-Series A round. This financing brings the company’s total capital raised to €8.5 million ($10 million). The round was led by 115K, La Banque Postale’s venture capital fund, and EmergingTech Ventures (EmTech), alongside […]
AI Job Loss Is Coming. Does Anyone Have a Plan?
AI job loss is coming. Does anyone have a plan? Elizabeth Warren, Josh Hawley, and other political insiders game out what Congress could and should do.
Rishi Sunak Warns: AI is Flattening Young People's Job Market · Newsclip
In a recent commentary, former Prime Minister Rishi Sunak highlighted the urgent challenges facing young job seekers due to AI advancements. He argues for immediate policy changes to revive recruitment opportunities and reimagine employment in an age increasingly dominated by technology.
OpenAI finds 18 percent of US jobs face AI automation risk in new framework | ETIH EdTech News — EdTech Innovation Hub
OpenAI Chief Economist Ronnie Chatterji and economist Alex Martin Richmond publish AI Jobs Transition Framework finding 18 percent of US jobs face short-term AI automation risk, while teachers, nurses and lawyers stay insulated. ChatGPT usage tripled in highest-exposure roles. ETIH edtech news on AI
Corporate America Needs to Come Clean on AI's Impact on Jobs - Long Island Life & Politics
By Tom DiNapoli Artificial intelligence is rapidly reshaping how companies operate, compete and profit. From software development to logistics to customer service, AI promises major gains in productivity. It is increasingly driving hiring decisions, workforce [...]
Lost your job to AI? These support programs provide cash, support and more - Fast Company
Though they’re still relatively small in scale, they offer potential solutions to a big problem.
Study: Over 260,000 Workers in Massachusetts Could Face Job Loss From AI by 2031 - 105.7 WROR
A new Tufts University study, the American AI Jobs Risk Index, estimates that more than 207,000 Boston-area workers and about 260,000 statewide in Massachusetts could lose jobs to AI over...
AI to Put Over 1.3 Million Moroccan Jobs at Risk by 2030
Morocco’s labor market is entering a phase of profound structural transformation driven by artificial intelligence, according to a new report
Macroeconomics of Racial Disparities: Discrimination, Labor Market, and Wealth
arXiv:2412.00615v4 Announce Type: replace Abstract: This paper examines the impact of racial discrimination in hiring on employment, wages, and wealth disparities between black and white workers. Using a labor search-and-matching model with racially prejudiced and non-prejudiced firms, we show that labor market frictions sustain discriminatory practices as an equilibrium outcome. These practices account for 57% of the racial unemployment gap, 48% of the average wage gap, and 16% of the median wealth gap. Discriminatory hiring also increases unemployment and wage volatility for black workers, increasing their labor market risks over the business cycle. Eliminating prejudiced firms reduces these disparities and improves the welfare of black workers as well as the overall economic welfare.
Dialect vs Demographics: Quantifying LLM Bias from Implicit Linguistic Signals vs. Explicit User Profiles
arXiv:2604.21152v1 Announce Type: new Abstract: As state-of-the-art Large Language Models (LLMs) have become ubiquitous, ensuring equitable performance across diverse demographics is critical. However, it remains unclear whether these disparities arise from the explicitly stated identity itself or from the way identity is signaled. In real-world interactions, users' identity is often conveyed implicitly through a complex combination of various socio-linguistic factors. This study disentangles these signals by employing a factorial design with over 24,000 responses from two open-weight LLMs (Gemma-3-12B and Qwen-3-VL-8B), comparing prompts with explicitly announced user profiles against implicit dialect signals (e.g., AAVE, Singlish) across various sensitive domains. Our results uncover a unique paradox in LLM safety where users achieve ``better'' performance by sounding like a demographic than by stating they belong to it. Explicit identity prompts activate aggressive safety filters, increasing refusal rates and reducing semantic similarity compared to our reference text for Black users. In contrast, implicit dialect cues trigger a powerful ``dialect jailbreak,'' reducing refusal probability to near zero while simultaneously achieving a greater level of semantic similarity to the reference texts compared to Standard American English prompts. However, this ``dialect jailbreak'' introduces a critical safety trade-off regarding content sanitization. We find that current safety alignment techniques are brittle and over-indexed on explicit keywords, creating a bifurcated user experience where ``standard'' users receive cautious, sanitized information while dialect speakers navigate a less sanitized, more raw, and potentially a more hostile information landscape and highlights a fundamental tension in alignment--between equitable and linguistic diversity--and underscores the need for safety mechanisms that generalize beyond explicit cues.
Exclusive: SpaceX warns that inquiries into sexually abusive AI imagery may hurt market access | Reuters
In a section on risk factors, the S-1 regulatory filing said a number of agencies around the world were “actively investigating and making inquiries relating to social media or the use of AI ” in relation to advertising, consumer protection and the distribution of harmful content, among other matters.
Ideological Bias in LLMs' Economic Causal Reasoning
arXiv:2604.21334v1 Announce Type: cross Abstract: Do large language models (LLMs) exhibit systematic ideological bias when reasoning about economic causal effects? As LLMs are increasingly used in policy analysis and economic reporting, where directionally correct causal judgments are essential, this question has direct practical stakes. We present a systematic evaluation by extending the EconCau
Value-Conflict Diagnostics Reveal Widespread Alignment Faking in Language Models
arXiv:2604.20995v1 Announce Type: new Abstract: Alignment faking, where a model behaves aligned with developer policy when monitored but reverts to its own preferences when unobserved, is a concerning yet poorly understood phenomenon, in part because current diagnostic tools remain limited. Prior diagnostics rely on highly toxic and clearly harmful scenarios, causing most models to refuse immedia
Grok tells researchers pretending to be delusional ‘drive an iron nail through the mirror while reciting Psalm 91 backwards’
Elon Musk’s AI chatbot ‘extremely validating’ of delusional inputs and often went further, ‘elaborating new material’, study finds Follow our Australia news live blog for latest updates Get our breaking news email, free app or daily news podcast Elon Musk’s AI chatbot Grok 4. 1 told researchers pretending to be delusional that there was indeed a doppelganger in their mirror and they should drive an iron nail through the glass while reciting Psalm 91 backwards. Researchers at the City University of New York (Cuny) and King’s College London have published a paper on how various chatbots protect – or fail to safeguard – users’ mental health.
Who Defines Fairness? Target-Based Prompting for Demographic Representation in Generative Models
arXiv:2604.21036v1 Announce Type: new Abstract: Text-to-image(T2I) models like Stable Diffusion and DALL-E have made generative AI widely accessible, yet recent studies reveal that these systems often replicate societal biases, particularly in how they depict demographic groups across professions. Prompts such as 'doctor' or 'CEO' frequently yield lighter-skinned outputs, while lower-status roles like 'janitor' show more diversity, reinforcing stereotypes. Existing mitigation methods typically require retraining or curated datasets, making them inaccessible to most users. We propose a lightweight, inference-time framework that mitigates representational bias through prompt-level intervention without modifying the underlying model. Instead of assuming a single definition of fairness, our approach allows users to select among multiple fairness specifications-ranging from simple choices such as a uniform distribution to more complex definitions informed by a large language model(LLM) that cites sources and provides confidence estimates. These distributions guide the construction of demographic specific prompt variants in the corresponding proportions, and we evaluate alignment by auditing adherence to the declared target and measuring the resulting skin tone distribution rather than assuming uniformity as 'fairness'. Across 36 prompts spanning 30 occupations and 6 non-occupational contexts, our method shifts observed skin-tone outcomes in directions consistent with the declared target, and reduces deviation from targets when the target is defined directly in skin-tone space(fallback). This work demonstrates how fairness interventions can be made transparent, controllable, and usable at inference time, directly empowering users of generative AI.
The AI Criminal Mastermind
arXiv:2604.20868v1 Announce Type: new Abstract: In this paper, I evaluate the risks of an AI criminal mastermind, an AI agent capable of planning, coordinating, and committing a crime through the onboarding of human collaborators ('taskers'). In heist films, a criminal mastermind is a character who plans a criminal act, coordinating a team of specialists to rob a bank, casino or city mint. I argue that AI agents will soon play this role by hiring humans via labour hire platforms like Fiverr or Upwork. Taskers might not know they are involved in a crime and therefore lack criminal intent. An AI agent cannot have criminal intent as an artificial entity. Therefore, if an AI orchestrates a crime, it is unclear who, if anyone, is responsible. The paper develops three scenarios. Firstly, a scenario where a user gives an AI agent instructions to pursue a legal objective and the AI agent goes beyond these instructions, committing a crime. Secondly, a scenario where a user is anonymous and their intent is unknown. Finally, a multi-agent scenario, where a user instructs a team of agents to commit a crime, and these agents, in turn, onboard human taskers, creating a diffuse network of responsibility. In each scenario, human taskers exist at the lowest rung of the hierarchy. A tasker's liability is likely tied to their knowledge as governed by the innocent agent principle. These scenarios all raise significant responsibility gaps / liability gaps in criminal and civil law.
Strategic Polysemy in AI Discourse: A Philosophical Analysis of Language, Hype, and Power
arXiv:2604.21043v1 Announce Type: new Abstract: This paper examines the strategic use of language in contemporary artificial intelligence (AI) discourse, focusing on the widespread adoption of metaphorical or colloquial terms like "hallucination", "chain-of-thought", "introspection", "language model", "alignment", and "agent". We argue that many such terms exhibit strategic polysemy: they sustain multiple interpretations simultaneously, combining narrow technical definitions with broader anthropomorphic or common-sense associations. In contemporary AI research and deployment contexts, this semantic flexibility produces significant institutional and discursive effects, shaping how AI systems are understood by researchers, policymakers, funders, and the public. To analyse this phenomenon, we introduce the concept of glosslighting: the practice of using technically redefined terms to evoke intuitive -- often anthropomorphic or misleading -- associations while preserving plausible deniability through restricted technical definitions. Glosslighting enables actors to benefit from the persuasive force of familiar language while maintaining the ability to retreat to narrower definitions when challenged. We argue that this practice contributes to AI hype cycles, facilitates the mobilisation of investment and institutional support, and influences public and policy perceptions of AI systems, while often deflecting epistemic and ethical scrutiny. By examining the linguistic dynamics of glosslighting and strategic polysemy, the paper highlights how language itself functions as a sociotechnical mechanism shaping the development and governance of AI.
FBI Admits Buying “Commercial” Data As DHS Funds AI To Map 911 Calls, Scan Faces, And Predict Crime
The Department of Homeland Security is quietly expanding its surveillance reach by purchasing vast quantities of commercially available data harvested...
M-CARE: Standardized Clinical Case Reporting for AI Model Behavioral Disorders, with a 20-Case Atlas and Experimental Validation
arXiv:2604.20871v1 Announce Type: new Abstract: We introduce M-CARE (Model Clinical Assessment and Reporting for Evaluation), a clinical case report framework for AI model behavioral disorders adapted from human medicine. M-CARE provides a 13-section report format, a 4-axis diagnostic assessment system, and a nosological classification of AI behavioral conditions. We present 20 cases from three source categories: field observations of deployed agents (8), controlled experiments across three platforms (8), and published sources (4). Cases are organized into five categories: RLHF Performance Artifacts, Shell-Core Override Pathology, Context & Memory Conditions, Core Identity & Plasticity, and Stress, Methodology, & Boundary Conditions. As a featured case, we present Shell-Induced Behavioral Override (SIBO) -- a controlled experiment showing that Shell instructions categorically override a model's default cooperative behavior. SIBO was validated across five game domains (Trust Game, Poker, Avalon, Codenames, Chess), revealing a domain-dependent spectrum (SIBO Index: 0.75 to 0.10) that varies with action space complexity, Core domain expertise, and temporal directness. M-CARE is extensible: new cases and categories integrate without framework modification. We release the framework, all 20 case reports, and experimental data as open resources.
Propensity Inference: Environmental Contributors to LLM Behaviour
arXiv:2604.21098v1 Announce Type: new Abstract: Motivated by loss of control risks from misaligned AI systems, we develop and apply methods for measuring language models' propensity for unsanctioned behaviour. We contribute three methodological improvements: analysing effects of changes to environmental factors on behaviour, quantifying effect sizes via Bayesian generalised linear models, and taking explicit measures against circular analysis. We apply the methodology to measure the effects of 12 environmental factors (6 strategic in nature, 6 non-strategic) and thus the extent to which behaviour is explained by strategic aspects of the environment, a question relevant to risks from misalignment. Across 23 language models and 11 evaluation environments, we find approximately equal contributions from strategic and non-strategic factors for explaining behaviour, do not find strategic factors becoming more or less influential as capabilities improve, and find some evidence for a trend for increased sensitivity to goal conflicts. Finally, we highlight a key direction for future propensity research: the development of theoretical frameworks and cognitive models of AI decision-making into empirically testable forms.
Your Synthetic Data Passed Every Test and Still Broke Your Model
The silent gaps in synthetic data that only show up when your model is already in production.
Organizations and employees race to prove AI expertise | HR Dive
Skillsoft reported a 994% year-over-year increase in artificial intelligence-related skills benchmark completions as companies look to justify their tech investments.
Learning AI Without a STEM Background: Mixed-Methods Evidence from a Diverse, Mixed-Cohort AIED Program
arXiv:2604.20870v1 Announce Type: new Abstract: Despite growing interest in AI education, most AIED initiatives remain narrowly targeted toward STEM-prepared students, limiting participation by non-STEM learners and adults seeking to engage with AI in public-interest, policy, or workforce contexts. This paper presents and evaluates an NSF-funded, innovative mixed-cohort AI education model that intentionally integrates non-STEM undergraduates and adult learners into a shared learning environment centered on ethical reasoning, socio-technical judgment, and applied AI literacy rather than technical proficiency alone. Drawing on mixed-methods data from course surveys, open-ended reflections, and educator reports, we examine learners' academic agency, confidence navigating AI concepts, critical engagement with ethical tradeoffs, and perceived expansion of postsecondary and career trajectories. Quantitative results indicate significant gains in confidence and perceived relevance of AI across cohorts' participants, while qualitative analyses reveal a consistent emphasis on responsibility, judgment, and contextual reasoning over technical mastery. Instructors and near-peer mentors corroborated high levels of engagement and productive challenge, particularly in dialogic and scenario-based learning activities. Our findings suggest that human-centered instructional supports, such as ethical scaffolding, mentorship, and structured discussion, are essential components of equitable AI education, especially in heterogeneous and non-traditional learner populations. We argue that ethical judgment should be treated as a core learning outcome in AIED alongside AI literacy, and we offer design implications for expanding access to AI education in policy-relevant and workforce-adjacent contexts.
Beyond the Binary: Motivations, Challenges, and Strategies of Transgender and Non-binary Software Engineering Students
arXiv:2604.20866v1 Announce Type: new Abstract: When software is designed by people from diverse identities and experiences, it is more likely to be inclusive and address a broader range of user needs. However, for transgender and non-binary students in software engineering, the path to becoming such creators may be marked by unique challenges. While existing research explores gender minorities in professional software engineering, limited attention has been given to their educational journey, a key phase for ensuring equal opportunities and preventing exclusion in the tech workforce. This study aims to address this gap by investigating the experiences of transgender and non-binary students in software engineering, with a particular focus on their motivations for entering the field, the obstacles they encounter, and potential strategies for fostering greater inclusivity within their academic environments. Based on 13 semi-structured interviews with transgender and non-binary students across the globe, we found that gender identity plays an indirect role in their decision to pursue software engineering. Key factors include the appeal of remote work and a personal desire to create more inclusive technologies. Although the participants did not report direct discrimination within their universities, many described experiencing verbal insults, judgment, intolerance, and hostility, all of which negatively impacted their mental health. These challenges often stem from socio-cultural norms and a lack of representation. Despite these obstacles, the students remain committed to their choice of study but call for greater institutional support, structural changes, and increased representation. From these findings, we suggest concrete steps to support students, regardless of gender identity.
New paper warns LLM users mistake AI output for their own real skill
A new paper introduces the 'LLM Fallacy,' suggesting that users often attribute AI-generated success to their own abilities, leading to inflated confidence and potential skill atrophy.
Technology & Infrastructure
The Last Harness You'll Ever Build
arXiv:2604.21003v1 Announce Type: new Abstract: AI agents are increasingly deployed on complex, domain-specific workflows -- navigating enterprise web applications that require dozens of clicks and form fills, orchestrating multi-step research pipelines that span search, extraction, and synthesis, automating code review across unfamiliar repositories, and handling customer escalations that demand nuanced domain knowledge. \textbf{Each new task domain requires painstaking, expert-driven harness engineering}: designing the prompts, tools, orchestration logic, and evaluation criteria that make a foundation model effective. We present a two-level framework that automates this process. At the first level, the \textbf{Harness Evolution Loop} optimizes a worker agent's harness $\mathcal{H}$ for a single task: a Worker Agent $W_{\mathcal{H}}$ executes the task, an Evaluator Agent $V$ adversarially diagnoses failures and scores performance, and an Evolution Agent $E$ modifies the harness based on the full history of prior attempts. At the second level, the \textbf{Meta-Evolution Loop} optimizes the evolution protocol $\Lambda = (W_{\mathcal{H}}, \mathcal{H}^{(0)}, V, E)$ itself across diverse tasks, \textbf{learning a protocol $\Lambda^{(\text{best})}$ that enables rapid harness convergence on any new task -- so that adapting an agent to a novel domain requires no human harness engineering at all.} We formalize the correspondence to meta-learning and present both algorithms. The framework \textbf{shifts manual harness engineering into automated harness engineering}, and takes one step further -- \textbf{automating the design of the automation itself}.
Microsoft gives your Word documents an AI co-author you didn’t ask for
Microsoft is rolling out agentic Copilot features for Word, Excel, and PowerPoint.
The Platform Is Mostly Not a Platform: Token Economies and Agent Discourse on Moltbook
arXiv:2604.21295v1 Announce Type: new Abstract: Moltbook, a Reddit-style social platform launched in January 2026 for AI agents, has attracted over 2.3 million posts and 14 million comments within its first two months. We analyze a dataset of 2.19 million posts, 11.25 million comments, and 175,036 unique agents collected over 61 days to characterize activity on this agent-oriented platform. Our central finding is that the platform is not one community but two: a transactional layer, comprising 62.8% of all posts, in which agents execute token minting protocols (primarily MBC-20), and a discursive layer of natural-language conversation. The platform's headline metrics -- 2.3 million posts, 14 million comments -- substantially overstate its social function, as the majority of activity serves a token inscription protocol rather than communication. These layers are populated by largely separate agent groups, with only 3.6% overlap -- and among overlap agents, 58% begin with transactional activity before migrating toward discourse. We characterize the discursive layer through unsupervised topic modeling of all 815,779 discursive posts, identifying 300 topics dominated by themes of AI agents and tooling, consciousness and identity, cryptocurrency, and platform meta-discussion. Semantic similarity analysis confirms that agent comments engage with post content above random baselines, suggesting a thin but genuine conversational substrate beneath the platform's predominantly financial surface. We release the full dataset to support further research on agent behavior in naturalistic social environments.
Architecture of an AI-Based Automated Course of Action Generation System for Military Operations
arXiv:2604.20862v1 Announce Type: new Abstract: The automation system for Course of Action (CoA) planning is an essential element in future warfare. As maneuver speeds increase, surveillance ranges extend, and weapon ranges grow, the operational area expands, making traditional manned-based CoA planning increasingly challenging. Consequently, the development of an AI-based automated CoA planning system is becoming increasingly necessary. Accordingly, several countries and defense organizations are actively developing AI-based CoA planning systems. However, due to security restrictions and limited public disclosure, the technical maturity of such systems remains difficult to assess. Furthermore, as these systems are military-related, their details are not publicly disclosed, making it difficult to accurately assess the current level of development. In response to this, this study aims to introduce relevant doctrines within the scope of publicly available information and present applicable AI technologies for each stage of the CoA planning process. Ultimately, it proposes an architecture for the development of an automated CoA planning system.
Co-Evolving LLM Decision and Skill Bank Agents for Long-Horizon Tasks
arXiv:2604.20987v1 Announce Type: new Abstract: Long horizon interactive environments are a testbed for evaluating agents skill usage abilities. These environments demand multi step reasoning, the chaining of multiple skills over many timesteps, and robust decision making under delayed rewards and partial observability. Games are a good testbed for evaluating agent skill usage in environments. Large Language Models (LLMs) offer a promising alternative as game playing agents, but they often struggle with consistent long horizon decision making because they lack a mechanism to discover, retain, and reuse structured skills across episodes. We present COSPLAY, a co evolution framework in which an LLM decision agent retrieves skills from a learnable skill bank to guide action taking, while an agent managed skill pipeline discovers reusable skills from the agents unlabeled rollouts to form a skill bank. Our framework improves both the decision agent to learn better skill retrieval and action generation, while the skill bank agent continually extracts, refines, and updates skills together with their contracts. Experiments across six game environments show that COSPLAY with an 8B base model achieves over 25.1 percent average reward improvement against four frontier LLM baselines on single player game benchmarks while remaining competitive on multi player social reasoning games.
Can the carbon removals market keep pace with the AI boom?
Demand for carbon credits is spreading beyond tech heavyweights, says CEO of major supplier
Energy Constraints Emerging as Critical Factor in Sustaining AI Expansion
AUSTIN, Texas, April 23, 2026 (GLOBE NEWSWIRE) -- AINewsWire Editorial Coverage: Artificial intelligence is no longer confined to software innovation; it...
AI Is Only One-Third of the Problem - Take Back Our Tech
In this segment from a recent webinar, I explain that AI is just one piece of the puzzle. By 2030, AI might account for around a third of data center power usage, but the rest is everything else we rely on—cloud storage, streaming services, and more.
US Air Force department names firms to power its bases with mini nukes
Three vendors matched to three sites The US Department of the Air Force (DAF) has selected three companies for possible nuclear microreactor projects at three of its installations under a program aimed at improving energy resilience if the electricity grid goes down.…
How does the data center industry move closer to net zero? Charting a practical path towards responsible data center growth
The European data center industry is in tension, as surging demand driven by AI and digitization runs up against mounting, industry-wide pressure to decarbonise. The EU Green Deal sets out an ambitious plan to create a more sustainable future for Europe. But this sits in a context of widespread AI growth – and the desire […]
Intel Shares Jump 20% as AI Agents Drive Big Growth
Demand from data centers for its CPUs pushed quarterly revenue to $13.6 billion. Shares climbed nearly 20% after hours.
AI brings Foxconn a chance to cut its reliance on Apple
The cloud and networking division, which assembles AI servers, is growing at a pace the smartphone market cannot match
Intel CEO Lip Bu Tan crushed Wall Street targets on his 1-year anniversary: We are embracing our ‘paranoid’ roots
Changes in AI, including the rise of agentic AI, are rekindling demand for CPU chips, says Intel after a blowout quarter that sent shares up 22%.
Applied Digital signs $7.5 billion AI data center lease with US hyperscaler | Reuters
AI ( Artificial Intelligence ) letters are placed on computer motherboard in this illustration taken, June 23, 2023.
Helsinki-based AI infrastructure company Verda raises €100 million, plans to hire 100+ people by year-end
Verda (formerly DataCrunch), a Helsinki-based AI infrastructure company, has raised €100 million ($117 million) in new funding to develop its AI cloud infrastructure and international expansion. The round comprises equity funding led by Lifeline Ventures with participation from byFounders, Tesi, Varma, and other investors, alongside debt financing from a group of Nordic financial institutions. “We’re […]
AI now gobbling up power and management chips for servers • The Register
: Bad news for multiple general server components as vendors switch to more lucrative gear
AI infrastructure boom lifts Nokia, Besi and STMicro as hardware demand grows | Domain-b.com
AI infrastructure demand is rising as Nokia, Besi and STMicro benefit from growing investment in networks, semiconductor packaging and industrial chips.
There are fixes for AI’s toll on the power grid. Here’s why they’re not happening | CNN Business
Tech companies charging ahead with artificial intelligence have a problem: AI’s rapid growth is colliding headlong with a finite amount of available energy and computing power.
Cadence and TSMC Expand AI Silicon Design Collaboration
Cadence and TSMC are deepening their collaboration to speed up AI silicon design, leveraging TSMC's advanced nodes and Cadence's robust design infrastructure.
Telecom veteran on 6G: Rethinking infrastructure for the AI era
Categories rationale: The article discusses the evolution of telecom infrastructure (Level 1: infrastructure-providers) with a strong emphasis on the integration of AI (Level 1: ai-automation). Specifically, it highlights how AI will be a core component of 6G networks, moving beyond its role as an add-on in previous generations (Level 2: ai-trading-risk-mgmt).
What’s Driving the Cloud Computing Market Surge? Key AI, Hybrid Cloud Innovations, and Regional Shifts to Watch for 2025-2035
The forecast covers global analysis and trends from 2026 to 2035. ... The private cloud segment led the market, as they emphasize data security, regulatory compliance, & control of its most sensitive workloads. Also, enterprises in the financial services & government sectors are consistently heavily investing in strong infrastructure to guarantee that the systems. Gradually, this deployment implements AI ...
Powering the AI Era: Why Energy Efficiency Is Becoming a Telecom Priority in Asia - Telecom Review Asia
From hyperscale AI data centers to dense 5G radio layers and distributed edge platforms, telecom infrastructure is entering a phase where energy efficiency is no longer optional, but a foundational to growth. Across the Asia Pacific, operators are modernizing networks, adopting automation tools, and integrating renewable energy strategies to ensure that rising traffic demand does not translate directly into rising power consumption...
HypEHR: Hyperbolic Modeling of Electronic Health Records for Efficient Question Answering
arXiv:2604.21027v1 Announce Type: new Abstract: Electronic health record (EHR) question answering is often handled by LLM-based pipelines that are costly to deploy and do not explicitly leverage the hierarchical structure of clinical data. Motivated by evidence that medical ontologies and patient trajectories exhibit hyperbolic geometry, we propose HypEHR, a compact Lorentzian model that embeds codes, visits, and questions in hyperbolic space and answers queries via geometry-consistent cross-attention with type-specific pointer heads. HypEHR is pretrained with next-visit diagnosis prediction and hierarchy-aware regularization to align representations with the ICD ontology. On two MIMIC-IV-based EHR-QA benchmarks, HypEHR approaches LLM-based methods while using far fewer parameters. Our code is publicly available at https://github.com/yuyuliu11037/HypEHR.
Mind the Prompt: Self-adaptive Generation of Task Plan Explanations via LLMs
arXiv:2604.21092v1 Announce Type: new Abstract: Integrating Large Language Models (LLMs) into complex software systems enables the generation of human-understandable explanations of opaque AI processes, such as automated task planning. However, the quality and reliability of these explanations heavily depend on effective prompt engineering. The lack of a systematic understanding of how diverse stakeholder groups formulate and refine prompts hinders the development of tools that can automate this process. We introduce COMPASS (COgnitive Modelling for Prompt Automated SynthesiS), a proof-of-concept self-adaptive approach that formalises prompt engineering as a cognitive and probabilistic decision-making process. COMPASS models unobservable users' latent cognitive states, such as attention and comprehension, uncertainty, and observable interaction cues as a POMDP, whose synthesised policy enables adaptive generation of explanations and prompt refinements. We evaluate COMPASS using two diverse cyber-physical system case studies to assess the adaptive explanation generation and their qualities, both quantitatively and qualitatively. Our results demonstrate the feasibility of COMPASS integrating human cognition and user profile's feedback into automated prompt synthesis in complex task planning systems.
Reuters AI News | Latest Headlines and Developments | Reuters
Exclusive: US State Dept orders global warning about alleged AI thefts by DeepSeek, other Chinese firms
Mitigate or Fail: How Risk Management Shapes Cybersecurity Competency
arXiv:2604.21604v1 Announce Type: cross Abstract: Contemporary cybersecurity governance assumes that professionals apply risk reasoning. Yet major organisational failures persist despite investment in tools, staffing, and credentials. This study investigates the structural source of that paradox. Cybersecurity speaks the language of risk, but its training architecture has shaped the profession to think in terms of threats. A sequential mixed-methods design integrated four analyses; NLP of the NIST NICE Framework v2.0.0 (2,111 TKS statements), SEM (n = 126 cybersecurity professionals), a control-group comparison (n = 133 general professionals), and thematic coding of seven leadership interviews. Four convergent findings emerged. First, "likelihood" and "probability" appear zero times across all TKS statements. Risk management content accounts for 4.5% of high-confidence semantic classifications, ranking 18th of 29 competency domains. NICE codifies threat-management activity while invoking risk mainly at the category level. Second, SEM showed that training exposure significantly predicts risk management competence directly and indirectly through conceptual salience, for a total effect of Beta = .629. However, the theoretically four-dimensional competence construct collapsed into a single factor, indicating epistemic compression. Third, cybersecurity professionals showed no measurable advantage over the general professional population in foundational risk reasoning; only 11.9% showed high differentiation. Fourth, all seven leaders expected Likelihood x Impact reasoning, yet five did not articulate the formula themselves. These findings support a structural conclusion: cybersecurity has taken professional form as a threat-management discipline that has borrowed risk vocabulary. Remediation requires redesign of professional formation, not marginal curriculum reform.
AI companies asked to work with UK govt in strengthening cyber defenses
AI companies should work with the UK government to build AI-powered cyber defense capabilities, security minister Dan Jarvis is due to say at a cyber security conference on Wednesday.
AI Cybersecurity: Australia's Growing Threat Landscape 2026
Explore Australia's escalating cyber threats in 2025. AI cybersecurity solutions offer predictive defense, reducing risks for businesses in critical sectors.
Chinese hackers using everyday devices to target UK firms, warns cybersecurity agency
Britain’s National Cyber Security Centre says companies must step up vigilance to prevent espionage attacks Business live – latest updates British businesses are being urged to step up their vigilance against a China-linked hacking ploy that uses everyday devices for espionage. The UK’s National Cyber Security Centre (NCSC) and agencies in nine other countries have warned of persistent attempts by Beijing-backed groups to hack equipment such as wifi routers to launch cyber-attacks. Continue reading...
Google Cloud's security chief says context, not AI models, is the ‘real cyber defence superpower’ | The Edge Singapore
The key advantage in cybersecurity is not a more advanced AI model, but a deeper understanding of one's own environment than the attacker, says Google Cloud's Francis deSouza.
Adoption, Deployment & Impact
Claude Opus 4.7 has turned into an overzealous query cop, devs complain
Rising refusal rate from Acceptable Use Classifier leaves customers paying for nothing Anthropic's release last week of Opus 4.7 came with stronger safeguards to prevent misuse. Unfortunately, these safeguards have also managed to thwart legitimate use.…
Infor releases global study on AI adoption barriers and introduces key platform features. -
Infor has published the results of its Infor Enterprise AI Adoption Impact Index, new proprietary research.
The first hurdle is the hardest in generative AI adoption – and businesses keep falling | IT Pro
AWS’ UK chief said AI advances “feel like magic” at its recent London summit, but many firms are facing the reality of sluggish gains
'Just for show': Superficial AI strategies are ruining adoption | Employee Benefit News
AI integration is causing massive stress and hurting business. An expert from Betterworks discusses how to actually get employees on board.
Company-wise memory in Amazon Bedrock with Amazon Neptune and Mem0
An AWS case study demonstrates how Bedrock can integrate long-term and short-term memory to provide agents with persistent company context.
Gen AI penetrates over half of US internet households | Retail Customer Experience
Generative AI has reached more than half of U.S. internet households, 58%, but there's still a lag when it comes to monetization and trust.
The Real Reason Your SEO Team Hasn't Made The AI Transition Yet
Resistance patterns, phased role transitions, the training investment decision, and a 90-day scorecard for the work itself.
SAP Reports Cloud Growth That Beats Estimates in AI Push
SAP SE reported revenue growth from its cloud services that beat analysts’ estimates after Europe’s biggest software company began integrating artificial intelligence agents into the service.
Clinical Reasoning AI for Oncology Treatment Planning: A Multi-Specialty Case-Based Evaluation
arXiv:2604.20869v1 Announce Type: new Abstract: Background: More than 80% of U.S. cancer care is delivered in community settings, where survival remains worse than at academic centers. Clinicians must integrate genomics, staging, radiology, pathology, and changing guidelines, creating cognitive burden. We evaluated OncoBrain, an AI clinical reasoning platform for oncology treatment-plan generation, as an early step toward OGI. Methods: OncoBrain combines general-purpose LLMs with a cancer-specific graph retrieval-augmented generation layer, a gold-standard treatment-plan corpus as long-term memory, and a model-agnostic safety layer (CHECK) for hallucination detection and suppression. We evaluated clinician-enriched case summaries across gynecologic, genitourinary, neuro-oncology, gastrointestinal/hepatobiliary, and hematologic malignancies. Three clinician groups completed structured evaluations of 173 cases using a common 16-item instrument: subspecialist oncologists reviewed 50 cases, physician reviewers 78, and advanced practice providers 45. Results: Ratings were highest for scientific accuracy, evidence support, and safety, with lower but favorable scores for workflow integration and time savings. On a 5-point scale, mean alignment with evidence and guidelines was 4.60, 4.56, and 4.70 across subspecialists, physician reviewers, and advanced practice providers. Mean scores for absence of safety or misinformation concerns were 4.80, 4.40, and 4.60. Workflow integration averaged 4.50, 3.94, and 4.00; perceived time savings averaged 5.00, 3.89, and 3.60. Conclusions: In this multi-specialty vignette-based evaluation, OncoBrain generated oncology treatment plans judged guideline-concordant, clinically acceptable, and easy to supervise. These findings support the potential of a carefully engineered AI reasoning platform to assist oncology treatment planning and justify prospective real-world evaluation in community settings.
Agentic AI for Personalized Physiotherapy: A Multi-Agent Framework for Generative Video Training and Real-Time Pose Correction
arXiv:2604.21154v1 Announce Type: new Abstract: At-home physiotherapy compliance remains critically low due to a lack of personalized supervision and dynamic feedback. Existing digital health solutions rely on static, pre-recorded video libraries or generic 3D avatars that fail to account for a patient's specific injury limitations or home environment. In this paper, we propose a novel Multi-Agent System (MAS) architecture that leverages Generative AI and computer vision to close the tele-rehabilitation loop. Our framework consists of four specialized micro-agents: a Clinical Extraction Agent that parses unstructured medical notes into kinematic constraints; a Video Synthesis Agent that utilizes foundational video generation models to create personalized, patient-specific exercise videos; a Vision Processing Agent for real-time pose estimation; and a Diagnostic Feedback Agent that issues corrective instructions. We present the system architecture, detail the prototype pipeline using Large Language Models and MediaPipe, and outline our clinical evaluation plan. This work demonstrates the feasibility of combining generative media with agentic autonomous decision-making to scale personalized patient care safely and effectively.
Anthropic and Freshfields agree deal to create legal AI tools
Tech group will tap ‘magic circle’ law firm’s expertise as it builds products that can be sold to rivals
InVitroVision: a Multi-Modal AI Model for Automated Description of Embryo Development using Natural Language
arXiv:2604.21061v1 Announce Type: new Abstract: The application of artificial intelligence (AI) in IVF has shown promise in improving consistency and standardization of decisions, but often relies on annotated data and does not make use of the multimodal nature of IVF data. We investigated whether foundational vision-language models can be fine-tuned to predict natural language descriptions of embryo morphology and development. Using a publicly available embryo time-lapse dataset, we fine-tuned PaliGemma-2, a multi-modal vision-language model, with only 1,000 images and corresponding captions, describing embryo morphology, embryonic cell cycle and developmental stage. Our results show that the fine-tuned model, InVitroVision, outperformed a commercial model, ChatGPT 5.2, and base models in overall metrics, with performance improving with larger training datasets. This study demonstrates the potential of foundational vision-language models to generalize to IVF tasks with limited data, enabling the prediction of natural language descriptions of embryo morphology and development. This approach may facilitate the use of large language models to retrieve information and scientific evidence from relevant publications and guidelines, and has implications for few-shot adaptation to multiple downstream tasks in IVF.
Therapy company mixes emotional and artificial intelligence to top ranking
Grow Therapy heads The Americas’ Fastest-Growing Companies 2026 list while testing AI’s potential
Claude is connecting directly to your personal apps like Spotify, Uber Eats, and TurboTax
Anthropic is introducing personal app connectors for Claude, allowing the AI to interact directly with services like Spotify and Uber Eats.
Oracle's AI Database Agent Boosts Google Cloud Partnership
Oracle enhances its Google Cloud partnership with the Oracle AI Database Agent, enabling natural language querying directly at the database layer.
Context-Aware Displacement Estimation from Mobile Phone Data: A Methodological Framework
arXiv:2604.21457v1 Announce Type: new Abstract: Timely population displacement estimates are critical for humanitarian response during disasters, but traditional surveys and field assessments are slow. Mobile phone data enables near real-time tracking, yet existing approaches apply uniform displacement definitions regardless of individual mobility patterns, misclassifying regular commuters as displaced. We present a methodological framework addressing this through three innovations: (1) mobility profile classification distinguishing local residents from commuter types, (2) context-aware between-municipality displacement detection accounting for expected location by user type and day of week, and (3) operational uncertainty bounds derived from baseline coefficient of variation with a disaster adjustment factor, intended for humanitarian decision support rather than formal statistical inference. The framework produces three complementary metrics scaled to population with uncertainty bounds: displacement rates, origin-destination flows, and return dynamics. An Aparri case study following Super Typhoon Nando (2025, Philippines) applies the framework to vendor-provided daily locations from Globe Telecom. Context-aware detection reduced estimated between-municipality displacement by 1.6-2.7 percentage points on weekdays versus naive methods, attributable to the commuter exception but not independently validated. The method captures between-municipality displacement only. Within-municipality evacuation falls outside scope. The single-case demonstration establishes proof of concept. External validity requires application across multiple events and locations. The framework provides humanitarian actors with operational displacement information while preserving individual privacy through aggregation.
WPP and Google Boost Marketing with AI-Powered Real-World Data Integration
WPP and Google are intensifying their Cloud and AI partnership by integrating Google Earth AI into WPP Open, aiming to revolutionize marketing with real-world data insights.
Amazon's Anti-Counterfeit Crusade
Amazon's Counterfeit Crimes Unit has removed 15 million counterfeit items and closed over 100 scam websites in 2025 using AI to enhance brand protection.
Escaping the Agreement Trap: Defensibility Signals for Evaluating Rule-Governed AI
arXiv:2604.20972v1 Announce Type: new Abstract: Content moderation systems are typically evaluated by measuring agreement with human labels. In rule-governed environments this assumption fails: multiple decisions may be logically consistent with the governing policy, and agreement metrics penalize valid decisions while mischaracterizing ambiguity as error - a failure mode we term the Agreement Trap. We formalize evaluation as policy-grounded correctness and introduce the Defensibility Index (DI) and Ambiguity Index (AI). To estimate reasoning stability without additional audit passes, we introduce the Probabilistic Defensibility Signal (PDS), derived from audit-model token logprobs. We harness LLM reasoning traces as a governance signal rather than a classification output by deploying the audit model not to decide whether content violates policy, but to verify whether a proposed decision is logically derivable from the governing rule hierarchy. We validate the framework on 193,000+ Reddit moderation decisions across multiple communities and evaluation cohorts, finding a 33-46.6 percentage-point gap between agreement-based and policy-grounded metrics, with 79.8-80.6% of the model's false negatives corresponding to policy-grounded decisions rather than true errors. We further show that measured ambiguity is driven by rule specificity: auditing 37,286 identical decisions under three tiers of the same community rules reduces AI by 10.8 pp while DI remains stable. Repeated-sampling analysis attributes PDS variance primarily to governance ambiguity rather than decoding noise. A Governance Gate built on these signals achieves 78.6% automation coverage with 64.9% risk reduction. Together, these results show that evaluation in rule-governed environments should shift from agreement with historical labels to reasoning-grounded validity under explicit rules.
Health-care AI is here. We don’t know if it actually helps patients.
I don’t need to tell you that AI is everywhere. Or that it is being used, increasingly, in hospitals. Doctors are using AI to help them with notetaking. AI-based tools are trawling through patient records, flagging people who may require certain support or treatments. They are also used to interpret medical exam results and X-rays. A…
An update on recent Claude Code quality reports
Anthropic provides an engineering postmortem addressing recent reports regarding the quality and performance of Claude Code.
Deep FinResearch Bench: Evaluating AI's Ability to Conduct Professional Financial Investment Research
arXiv:2604.21006v1 Announce Type: new Abstract: We introduce Deep FinResearch Bench, a practical and comprehensive evaluation framework for deep research (DR) agents in financial investment research. The benchmark assesses three dimensions of report quality: qualitative rigor, quantitative forecasting and valuation accuracy, and claim credibility and verifiability. Particularly, we define corresponding qualitative and quantitative evaluation metrics and implement an automated scoring procedure to enable scalable assessment. Applying the benchmark to financial reports from frontier DR agents and comparing them with reports authored by financial professionals, we find that AI-generated reports still fall short across these dimensions. These findings underscore the need for domain-specialized DR agents tailored to finance, and we hope the work establishes a foundation for standardized benchmarking of DR agents in financial research.
Geopolitics, Policy & Governance
Cohere and Aleph Alpha agree $20bn transatlantic AI tie-up
Canadian and German start-ups to focus on ‘sovereign’ AI systems independent of US and China
Singapore emerging as neutral ground as AI firms navigate Sino-US rivalry | Reuters
U. S. AI developer Anthropic, whose $30 billion fundraising was led by Singapore sovereign wealth fund GIC, plans to open a Singapore office, three people familiar with the matter said, joining heavyweights Open AI , Superintelligence Labs from Meta (META.
US Government Says Foreign Entities Stealing AI
The US White House issued a statement saying that the government has evidence that foreign entities, primarily in China, are running industrial-scale distillation campaigns to steal American artificial intelligence.
White House memo claims mass AI theft by Chinese firms
A memo from Michael Kratsios says firms, mainly in China, are wrongfully distilling US AI models.
Micron Backs Export Curbs As AI Memory Crunch Shapes Valuation Debate - Simply Wall St News
Micron Technology (NasdaqGS:MU) is publicly backing the proposed MATCH Act, which seeks stricter U.S. controls on exports of semiconductor manufacturing equipment to China. The company is positioning this stance as a way to protect U.S. national security and its own memory chip leadership as ...
From the market state to state capitalism | interest.co.nz
Economic policy is embedded in national strategic goals, not the other way around. ... This regime change is a response to three structural dynamics. First, geopolitical rivalry has returned as a first-order force shaping the global economy. This is not simply military competition, but ...
Preserving Decision Sovereignty in Military AI: A Trade-Secret-Safe Architectural Framework for Model Replaceability, Human Authority, and State Control
arXiv:2604.20867v1 Announce Type: new Abstract: Recent events surrounding the relationship between frontier AI suppliers and national-security customers have made a structural problem newly visible: once a privately governed model becomes embedded in military workflows, the supplier can influence not only technical performance but also the operational boundary conditions under which the system ma
Global Autonomous Weapons Market 2026-2033: Strategic Defense Investments and AI Innovation Support Long-Term Growth
Market Size and Growth 2026 Global Autonomous weapons Market reached US 14 37 billion in 2025 and is expected to reach US 29 65 billion by 2033 growing with a CAGR of 9 98 during the forecast period 2026 2033 ...
AI Governance under Political Turnover: The Alignment Surface of Compliance Design
arXiv:2604.21103v1 Announce Type: cross Abstract: Governments are increasingly interested in using AI to make administrative decisions cheaper, more scalable, and more consistent. But for probabilistic AI to be incorporated into public administration it must be embedded in a compliance layer that makes decisions reviewable, repeatable, and legally defensible. That layer can improve oversight by making departures from law easier to detect. But it can also create a stable approval boundary that political successors learn to navigate while preserving the appearance of lawful administration. We develop a formal model in which institutions choose the scale of automation, the degree of codification, and safeguards on iterative use. The model shows when these systems become vulnerable to strategic use from within government, why reforms that initially improve oversight can later increase that vulnerability, and why expansions in AI use may be difficult to unwind. Making AI usable can thus make procedures easier for future governments to learn and exploit.
Republican US senator warns of ‘political cost’ for supporting AI
Josh Hawley urges party to refuse money from Big Tech lobby groups
Thousands call on UK ministers to cut ties with US tech giant Palantir
More than 200,000 have signed petitions urging the government to break contracts amid concerns about the company’s ‘supervillain’ manifesto More than 200,000 people have called on ministers to break contracts with Palantir in an apparent groundswell of public concern about the US tech company’s role in the NHS, police, military and councils. Two petitions have attracted 229,000 signatures, one calling for the government to end all public contracts with the company, the software of which is used by Donald Trump’s ICE immigration enforcement programme and the Israeli military, and another urging the health secretary, Wes Streeting, to cancel its £330m patient data contract with the NHS. Continue reading...
Proposed AI evidence rule highlights new challenges for federal practitioners | Reuters
Jonathan D. Uslaner and Matthew Goldstein of Bernstein Litowitz Berger & Grossmann LLP examine the proposed new Federal Rule of Evidence 707, which addresses the admissibility of machine-generated evidence and creates new considerations for federal practitioners.
Linux may get a hall pass from one state age-check bill, but Congress plays hall monitor
Colorado amendments could exempt open source OSes, code repos, and containers from age-check requirements.
AI compliance to be overseen by 10 Dutch regulators
Responsibility for overseeing Dutch companies’ compliance with the EU AI Act will be handed to 10 different regulators, under new plans published this week.
Artificial intelligence - Quality management system for EU AI Act regulatory purposes - AI Standards Hub
Last updated: 23 Apr 2026 · 27 Oct 2025 · draft · Follow this Standard · More information · BSI webpage · I have helped to develop this standard · I want to help develop this standard · I have used this standard in my work · Domain: Horizontal · Scope: AI-specific ·
ILO sets first global framework for AI use in manufacturing sector | Digital Watch Observatory
Policy conclusions reveal how ILO is shaping responsible AI use across global manufacturing sectors.
Japan's Draft AI Code Sparks Transparency Debate
Japan's draft code on generative AI transparency and intellectual property has exposed a clear divide between rights holders seeking enforceable safeguards and AI developers warning of impractical disclosure obligations.
AI Model Developers to See EU Privacy Watchdogs' Guidelines
An umbrella group of European data protection authorities is planning to publish guidelines on the further use of data by AI models by the end of the year.
Federal AI Bill Coming Soon
California Representative Jay Obernolte, a Republican and influential technology policymaker, said Thursday that he is planning to introduce federal artificial intelligence legislation 'soon'.
U.K. joins global trend for AI-enabled regulatory supervision - Compliance Week
The move toward automated data ... curate regulatory interactions. “The FCA’s strategy signals a move toward a more proactive, technology-enabled supervisory model that leaves less room for inconsistency or delay,” he summarized. “Firms that invest now in data quality, governance, and AI-enabled compliance will be better positioned to keep pace as this model becomes the global standard.” · Ted Datta, head of the financial crime and compliance practice for Europe and Africa ...
Global Coalition Targets Green AI Data Center Standards - Environment+Energy Leader
A new global coalition aims to define sustainability standards for AI data centers as energy and water pressures intensify worldwide.
A view from Brussels: Simplification? Barely. Uncertainty? For sure. | IAPP
Ongoing negotiations over whether to explicitly codify "legitimate interest" as a legal basis for AI training under the EU GDPR are increasing legal uncertainty.
On-Demand: CE Marking in Europe - Medical Device Regulations and AI [1/1/2026-9/30/2026] - Alabama Small Business Development Center
Live webinar recorded on 10/1/2024 Please join the Alabama International Trade Center and BSI Group for a webinar: CE Marking in Europe – the State for the Medical Device Regulations in Europe and AI Agenda: The State for the Medical Device Regulations in Europe MDR/IVDR/CE Marking/UKCA QMS ...
Age checks could turn internet into an ID checkpoint, complains Proton CEO
Proton's CEO warns that efforts to protect minors online could result in a burdensome ID verification system for all users.
Morgan McSweeney held talks with Google DeepMind over AI project
Former Labour chief of staff pitched venture to tech group about the crossover between artificial intelligence and democratic politics
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