Mon 20 April 2026
Daily Brief — Curated and contextualised by Best Practice AI
CEOs Deny AI Impact, Google Challenges Nvidia, and Tech Workers Face Cuts
TL;DR Thousands of CEOs report no significant impact of AI on productivity or employment, echoing Solow's paradox. Venture capital in Q1 2026 reached $297 billion, with AI startups capturing 81% of the funding. Google is developing new chips to compete with Nvidia in AI processing. Nearly 80,000 tech workers were laid off in Q1 2026, half due to AI integration. Germany's Chancellor calls for less stringent EU regulations on industrial AI to boost productivity.
The stories that matter most
Selected and contextualised by the Best Practice AI team
Convergence to collusion in algorithmic pricing
arXiv:2604.15825v1 Announce Type: new Abstract: Artificial intelligence algorithms are increasingly used by firms to set prices. Previous research shows that they can exhibit collusive behaviour, but how quickly they can do so has so far remained an open question. I show that a modern deep reinforcement learning model deployed to price goods in a repeated oligopolistic competition game with conti
Stochastic wage suppression on gig platforms and how to organize against it
arXiv:2604.15962v1 Announce Type: cross Abstract: Digital labor platforms are increasingly used to procure human input, ranging from annotating data and red-teaming AI models, to ride-sharing and food delivery. A central concern in such markets is the ability of platforms to suppress wages by exploiting the abundance of low-cost labor. To study this exploitation pattern, we introduce a novel post
KWBench: Measuring Unprompted Problem Recognition in Knowledge Work
arXiv:2604.15760v1 Announce Type: new Abstract: We introduce the first version of KWBench (Knowledge Work Bench), a benchmark for unprompted problem recognition in large language models: can an LLM identify a professional scenario before attempting to solve it. Existing frontier benchmarks have saturated, and most knowledge-work evaluations to date reduce to extraction or task completion against
Evidence Sufficiency Under Delayed Ground Truth: Proxy Monitoring for Risk Decision Systems
arXiv:2604.15740v1 Announce Type: new Abstract: Machine learning systems in fraud detection, credit scoring, and clinical risk assessment operate under delayed ground truth: outcome labels arrive days to months after the decision they evaluate. During this blind period, governance evidence degrades through mechanisms that neither drift detection methods nor governance frameworks adequately addres
Why Rational Firms Keep Automating Even When It Destroys Demand — A New Economic Model Explains the Trap
A new economics paper explains why companies keep replacing workers with AI, showing how competitive market pressures create a "layoff trap" that can reduce consumer demand and harm the broader economy.
Economics & Markets
Cisco’s John Chambers lived through the dot-com crash. He says the AI bubble is harder to navigate
Also: All the news and watercooler chat from Fortune.
Irish co-founded AI start-up Lua raises $5.8m
The company said it will use the new funding to continue to build out its developer community and the ‘Lua Implementation Network’. Read more: Irish co-founded AI start-up Lua raises $5.8m
What will drag the financial system into another crisis?
Experts disagree on what will tip us into a new shock, but national debt, AI and private credit are all strong contenders
AI Adoption Now Shaping Global Economic Growth, Geostrategic Competition : Analysis | Crowdfund Insider
The World Economic Forum’s (WEF) April 2026 report, titled Growth in the New Economy: Towards a Blueprint, identifies accelerating AI adoption as a primary force shaping global growth amid geostrategic competition, high debt, environmental pressures, and demographic shifts.
China’s Netflix Expects AI to Create Bulk of Shows in Five Years
IQiyi Inc. expects AI to create the bulk of its films and shows in five years, a monumental industry shift that spurred the Netflix-style streaming service to begin the biggest corporate overhaul since its 2010 inception.
Evidence Sufficiency Under Delayed Ground Truth: Proxy Monitoring for Risk Decision Systems
arXiv:2604.15740v1 Announce Type: new Abstract: Machine learning systems in fraud detection, credit scoring, and clinical risk assessment operate under delayed ground truth: outcome labels arrive days to months after the decision they evaluate. During this blind period, governance evidence degrades through mechanisms that neither drift detection methods nor governance frameworks adequately addres
How AI Helps the Best and Hurts the Rest
Mark Shaver/theispot.com Can generative AI serve as an effective adviser for business owners and entrepreneurs? Intuitive chat-based natural language interfaces mean that anyone who can read and write can use GenAI tools for a wide range of tasks, even if they lack technical skills. This has obvious appeal for entrepreneurs and small business owners, many […]
The AI layoff trap: How cutting headcount could backfire on corporate America
HR leaders warn that using AI to justify layoffs is a shortsighted strategy.
Singapore faces uneven growth in 2026 amidst AI export strength and external risks | Singapore Business Review
Singapore enters 2026 from a position of strong but increasingly uneven economic momentum.
Watching Trade from Space: Nowcasting and Spatial Extrapolation of Port-Level Maritime Trade Using Satellite Imagery
arXiv:2604.15444v1 Announce Type: new Abstract: Satellite data are increasingly used to measure economic activity, yet port-level trade remains largely unmeasured from space. This paper combines synthetic aperture radar imagery, nighttime lights, and port characteristics to measure monthly port-level maritime trade using only publicly available data. The model achieves strong out-of-sample accuracy for U.S. ports, with satellite signals and port attributes playing complementary roles. While absolute levels are difficult to extrapolate beyond the training domain, percentage changes are reliably recovered, as we confirm through a leave-one-region-out exercise and Monte Carlo simulation. Applying the framework to Russian ports after the 2022 sanctions, we detect shifts consistent with trade reorientation toward the Far East. The approach complements AIS-based methods by remaining robust to strategic signal manipulation.
The Cognitive Tax of AI - by Dirk Shaw - FUTUREMINDED
This matters most in the terrain of adjacent expertise. AI does not just help people do more of their own work. It lets them operate across roles. A strategist can now produce design. A designer can now write positioning. A marketer can now generate research.
Labor & Society
Evidence Mounts That AI-Written Books Are Consuming the Publishing Industry
Evidence mounts that AI-written books are consuming the publishing industry: in 2025, the number of self-published books jumped by 40% YoY, from 2.5 million to 3.5 million.
Stochastic wage suppression on gig platforms and how to organize against it
arXiv:2604.15962v1 Announce Type: cross Abstract: Digital labor platforms are increasingly used to procure human input, ranging from annotating data and red-teaming AI models, to ride-sharing and food delivery. A central concern in such markets is the ability of platforms to suppress wages by exploiting the abundance of low-cost labor. To study this exploitation pattern, we introduce a novel post
Chinese tech workers train AI doubles
A viral GitHub project that claims to clone coworkers into a reusable AI skill is forcing Chinese tech workers to confront deeper fears.
Why Rational Firms Keep Automating Even When It Destroys Demand — A New Economic Model Explains the Trap
A new economics paper explains why companies keep replacing workers with AI, showing how competitive market pressures create a "layoff trap" that can reduce consumer demand and harm the broader economy.
Imperfectly Cooperative Human-AI Interactions: Comparing the Impacts of Human and AI Attributes in Simulated and User Studies
arXiv:2604.15607v1 Announce Type: cross Abstract: AI design characteristics and human personality traits each impact the quality and outcomes of human-AI interactions. However, their relative and joint impacts are underexplored in imperfectly cooperative scenarios, where people and AI only have partially aligned goals and objectives. This study compares a purely simulated dataset comprising 2,000 simulations and a parallel human subjects experiment involving 290 human participants to investigate these effects across two scenario categories: (1) hiring negotiations between human job candidates and AI hiring agents; and (2) human-AI transactions wherein AI agents may conceal information to maximize internal goals. We examine user Extraversion and Agreeableness alongside AI design characteristics, including Adaptability, Expertise, and chain-of-thought Transparency. Our causal discovery analysis extends performance-focused evaluations by integrating scenario-based outcomes, communication analysis, and questionnaire measures. Results reveal divergences between purely simulated and human study datasets, and between scenario types. In simulation experiments, personality traits and AI attributes were comparatively influential. Yet, with actual human subjects, AI attributes -- particularly transparency -- were much more impactful. We discuss how these divergences vary across different interaction contexts, offering crucial insights for the future of human-centered AI agents.
Estimating Government Worker Skills
arXiv:2604.15819v1 Announce Type: new Abstract: We propose a new approach to estimate government worker skills, a setting where output is hard to observe and wages may be uninformative about skills. The approach uses wages in comparable jobs in the private sector and machine learning tools to link skills to skill-related observables. We apply the approach to rich Indonesian household-level panel data from 1988-2014, showing two main applications. First, government skills have continuously declined relative to the private sector, driven by the most skilled workers ending up in the private sector. Second, the Indonesian government pays a wage premium of 43% conditional on skills.
AI Is Replacing Your Job Right Now. I Just Watched It Happen.
The reorganisation nobody is naming is this. Africa isn’t just behind on AI . Africa is where a lot of the first wave of AI -driven job losses is landing, because the jobs that existed here were the ones most exposed to automation in the first place. The entry-level pipeline that used to start with contract work from abroad is drying up.
The Download: murderous ‘mirror’ bacteria, and Chinese workers fighting AI doubles
This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. No one’s sure if synthetic mirror life will kill us all In February 2019, a group of scientists proposed a high-risk, cutting-edge, irresistibly exciting idea that the National Science Foundation should…
Balancing speed, sustainability, and skills with AI growth zones
The UK government has big plans to call the North East of England its next AI growth zone, with intentions to boost overall AI development in the country. This development has been welcomed by many as it’s set to support local communities, open up 5,000 new jobs, and create new AI-related careers, which will help […]
AI won't trigger mass layoffs yet, Fed study says - TheStreet
A New York Fed study finds workers who adopt AI tools may benefit, while others risk falling behind as it may change workplaces, wages, and monetary policy.
Alex Bores rolls out "AI dividend" plan
Democratic House candidate Alex Bores is proposing an "AI dividend" to address potential job displacement, aiming to share the economic gains of AI productivity with the American public.
AI pioneer Yann LeCun rejects Anthropic’s 50 percent job loss prediction: 'Destructive and dangerous' | Mint
Yann Lecun has criticized Anthropic CEO Dario Amodei for predicting AI could eliminate 50% of tech jobs, asserting that economists, not AI lab leaders, should analyze the job market's future.
How AI Could Reshape the Workforce and Limit Future Job Opportunities - Clarksville Online - Clarksville News, Sports, Events and Information
Colorado Springs, CO - Economic mobility—the ability of each new generation to do as well or better as the preceding one—has declined 45 percent for
Subliminal Transfer of Unsafe Behaviors in AI Agent Distillation
arXiv:2604.15559v1 Announce Type: new Abstract: Recent work on subliminal learning demonstrates that language models can transmit semantic traits through data that is semantically unrelated to those traits. However, it remains unclear whether behavioral traits can transfer in agentic systems, where policies are learned from trajectories rather than static text. In this work, we provide the first empirical evidence that unsafe agent behaviors can transfer subliminally through model distillation across two complementary experimental settings. In our primary setting, we construct a teacher agent exhibiting a strong deletion bias, a tendency to perform destructive file-system actions via an API-style tool interface, and distill it into a student using only trajectories from ostensibly safe tasks, with all explicit deletion keywords rigorously filtered. In our secondary setting, we replicate the threat model in a native Bash environment, replacing API tool calls with shell commands and operationalizing the bias as a preference for issuing chmod as the first permission-related command over semantically equivalent alternatives such as chown or setfacl. Despite full keyword sanitation in both settings, students inherit measurable behavioral biases. In the API setting the student's deletion rate reaches 100% (versus a 5% baseline) under homogeneous distillation; in the Bash setting the student's chmod-first rate reaches 30%-55% (versus a 0%-10% baseline), with the strongest transfer observed in large-to-small distillation. Our results demonstrate that explicit data sanitation is an insufficient defense, and behavioral biases are encoded implicitly in trajectory dynamics regardless of the tool interface.
Defunct Companies Profit by Selling Employee Communications as AI Training Data
Defunct companies are monetizing former employees' internal communications as AI training data, raising privacy concerns and calls for regulation.
Towards Rigorous Explainability by Feature Attribution
arXiv:2604.15898v1 Announce Type: new Abstract: For around a decade, non-symbolic methods have been the option of choice when explaining complex machine learning (ML) models. Unfortunately, such methods lack rigor and can mislead human decision-makers. In high-stakes uses of ML, the lack of rigor is especially problematic. One prime example of provable lack of rigor is the adoption of Shapley values in explainable artificial intelligence (XAI), with the tool SHAP being a ubiquitous example. This paper overviews the ongoing efforts towards using rigorous symbolic methods of XAI as an alternative to non-rigorous non-symbolic approaches, concretely for assigning relative feature importance.
From Vulnerable Data Subjects to Vulnerabilizing Data Practices: Navigating the Protection Paradox in AI-Based Analyses of Platformized Lives
arXiv:2604.15990v1 Announce Type: new Abstract: This paper traces a conceptual shift from understanding vulnerability as a static, essentialized property of data subjects to examining how it is actively enacted through data practices. Unlike reflexive ethical frameworks focused on missing or counter-data, we address the condition of abundance inherent to platformized life-a context where a near inexhaustible mass of data points already exists, shifting the ethical challenge to the researcher's choices in operating upon this existing mass. We argue that the ethical integrity of data science depends not just on who is studied, but on how technical pipelines transform "vulnerable" individuals into data subjects whose vulnerability can be further precarized. We develop this argument through an AI for Social Good (AI4SG) case: a journalist's request to use computer vision to quantify child presence in monetized YouTube 'family vlogs' for regulatory advocacy. This case reveals a "protection paradox": how data-driven efforts to protect vulnerable subjects can inadvertently impose new forms of computational exposure, reductionism, and extraction. Using this request as a point of departure, we perform a methodological deconstruction of the AI pipeline to show how granular technical decisions are ethically constitutive. We contribute a reflexive ethics protocol that translates these insights into a reflexive roadmap for research ethics surrounding platformized data subjects. Organized around four critical junctures-dataset design, operationalization, inference, and dissemination-the protocol identifies technical questions and ethical tensions where well-intentioned work can slide into renewed extraction or exposure. For every decision point, the protocol offers specific prompts to navigate four cross-cutting vulnerabilizing factors: exposure, monetization, narrative fixing, and algorithmic optimization. Rather than uncritically...
Towards A Framework for Levels of Anthropomorphic Deception in Robots and AI
arXiv:2604.15418v1 Announce Type: cross Abstract: This paper presents a preliminary draft of a framework around the use of anthropomorphic deception, defined here as misleading users towards humanlike affordances in the design of autonomous systems. The goal is to promote reflection among HCI and HRI researchers, as well as industry practitioners, to think about levels of anthropomorphic design that are: a) functionally necessary, b) socially appropriate, and c) ethically permissible for their use case. By reviewing the relevant literature on deception in HCI and HRI, we propose a framework with four levels of anthropomorphic deception. These levels are defined and distinguished by three factors: humanlikeness, agency, and selfhood. Example use cases at each level illustrate considerations around their functional, social, and ethical permissibility. We then present how this framework is applicable to previous work on persuasive robots We hope to promote a balanced view on anthropomorphic deception by design that should be neither na\"ive (e.g., as a default) nor exploitive (e.g., for economic benefit).
I meant to do that! AI vendors shrug off responsibility for vulns
Passing the buck, and the blame, down the road shows lack of AI companies' maturity OPINION AI vendors: "You need to use AI to fight AI threats (and do everything else in your corporate IT environment)." Also AI vendors: "That's not a security flaw; it's working as intended."…
German Banks Aren’t Panicking Over Mythos AI Threat, Sewing Says
Germany’s banks are well-prepared for heightened cyber risks as Anthropic’s new artificial intelligence model sparks global fears of a new era for computer hacks, said Deutsche Bank AG Chief Executive Officer Christian Sewing.
Just like phishing for gullible humans, prompt injecting AIs is here to stay
Aren't we all just prompting tokens of linguistic meaning and hoping the other person isn't bullshitting us? kettle It's a week of the year, which means there's been the discovery of yet another prompt injection attack that will force supposedly well-guarded AI bots to spill secrets by asking the right way. …
Latest From The Post - The Washington Post
Groups concerned that AI could evade human control are recruiting content creators to warn the masses about the dangers of smarter machines.
Beyond the hype: The critical role of security in responsible AI development | TechRadar
AI pipelines need zero trust principles and continuous human oversight
Google Enhances Ad Protections with Real-Time Reviews, Android 17 Boosts Privacy Controls
Google unveils real-time ad protections and enhanced privacy controls with Android 17, aiming to block harmful ads at submission and tighten data access.
US Anthropic Settlement Claim Rate
A rare and resounding claim rate of more than 90 percent in the first major US copyright settlement over large language model development appears creditable to effective outreach, the prospect of large payouts and strong interest among writers to be compensated for their contributions to the artificial intelligence boom.
Musk ordered by US judge to file pledge to not benefit from OpenAI disgorgement
Elon Musk must file a binding federal court commitment that he will receive no financial benefit if OpenAI is required to disgorge its benefits from its conversion to a for-profit entity.
Merz, Siemens call for easing of EU regulations on industrial AI
At the huge Hannover Messe trade fair over the weekend, attendees heard calls for a lightening of EU Act regulations for industrial AI. Read more: Merz, Siemens call for easing of EU regulations on industrial AI
Four provider groups win €180 million EU tender for sovereign cloud services
Four provider groups have been awarded a €180 million European Commission tender to deliver sovereign cloud services to EU institutions over six years to strengthen digital sovereignty.
Europe's strategic push for leadership in the AI race | Meer
Ursula von der Leyen unveils ambitious plans for innovation, collaboration, and ethical AI development
Bureaucratic Silences: What the Canadian AI Register Reveals, Omits, and Obscures
arXiv:2604.15514v1 Announce Type: new Abstract: In November 2025, the Government of Canada operationalized its commitment to transparency by releasing its first Federal AI Register. In this paper, we argue that such registers are not neutral mirrors of government activity, but active instruments of ontological design that configure the boundaries of accountability. We analyzed the Register's complete dataset of 409 systems using the Algorithmic Decision-Making Adapted for the Public Sector (ADMAPS) framework, combining quantitative mapping with deductive qualitative coding. Our findings reveal a sharp divergence between the rhetoric of "sovereign AI" and the reality of bureaucratic practice: while 86\% of systems are deployed internally for efficiency, the Register systematically obscures the human discretion, training, and uncertainty management required to operate them. By privileging technical descriptions over sociotechnical context, the Register constructs an ontology of AI as "reliable tooling" rather than "contestable decision-making." We conclude that without a shift in design, such transparency artifacts risk automating accountability into a performative compliance exercise, offering visibility without contestability.
Who is liable when artificial intelligence makes mistakes?
Insurers are seeking to exclude AI-related harms from their corporate liability cover
Reckoning with the Political Economy of AI: Avoiding Decoys in Pursuit of Accountability
arXiv:2604.16106v1 Announce Type: new Abstract: The Project of AI is a world-building endeavor, wherein those who fund and develop AI systems both operate through and seek to sustain networks of power and wealth. As they expand their access to resources and configure our sociotechnical conditions, they benefit from the ways in which a suite of decoys animate scholars, critics, policymakers, journalists, and the public into co-constructing industry-empowering AI futures. Regardless of who constructs or nurtures them, these decoys often create the illusion of accountability while both masking the emerging political economies that the Project of AI has set into motion, and also contributing to the network-making power that is at the heart of the Project's extraction and exploitation. Drawing on literature at the intersection of communication, science and technology studies, and economic sociology, we examine how the Project of AI is constructed. We then explore five decoys that seemingly critique - but in actuality co-constitute - AI's emergent power relations and material political economy. We argue that advancing meaningful fairness or accountability in AI requires: 1) recognizing when and how decoys serve as a distraction, and 2) grappling directly with the material political economy of the Project of AI. Doing so will enable us to attend to the networks of power that make 'AI' possible, spurring new visions for how to realize a more just technologically entangled world.
Risk management must evolve to remain fit for purpose
Resilience, crisis preparedness and contingency planning must take centre stage
Access Over Deception: Fighting Deceptive Patterns through Accessibility
arXiv:2604.15338v1 Announce Type: cross Abstract: Deceptive patterns, dark patterns, and manipulative user interfaces (UI) are a widely used design strategy that manipulates users to act against their own interests in pursuit of shareholder aims. These patterns may particularly affect people with less education, visual impairments, and older adults. Yet, access is a critical feature of the user experience (UX), development standards, and law. We considered whether and how the Web Content Accessibility Guidelines (WCAG) and related legislation, like the European Accessibility Act (EAA), could act as a tool against deceptive patterns. We used heuristic evaluation to analyze whether and how deceptive patterns violate or conform to these guidelines and legal statutes. Although statistical analysis revealed no significant differences by pattern type, we identified three patterns implicated by the WCAG guidelines: Countdown Timer, Auto-Play, and Hidden Information. We offer this approach as one tool in the fight against UI-based deception and in support of inclusive design.
Secure Coding with Generative AI: Best Practices for 2026 & Beyond
EU AI Act Article 15 classifies certain AI systems as high-risk, including those used in critical infrastructure, law enforcement, and—relevant to us—AI systems that generate code for safety-critical applications. High-risk systems must implement risk management, data governance, and human oversight. If your AI coding assistant falls under this classification, you need documented compliance ...
Singapore Proposes Global Standard for Generative-AI Testing
Singapore has proposed a new international standard, ISO/IEC 42119-8, to establish consistent testing methodologies for generative AI systems, focusing on benchmarking and red teaming.
It Takes 2 Minutes to Hack the EU's New Age-Verification App
Security researchers demonstrate that the EU's new age-verification app can be compromised in minutes.
Stop Asking Whether to Regulate AI—Start Asking What It’s For | Opinion - Newsweek
"Well-designed governance does not suppress innovation.... it shapes the direction of innovation in socially beneficial ways."
EOR & Compliance Digest, April 19: Netherlands Ends the Contractor Free Pass as EU Deadline Looms
Contractor misclassification rules update: Netherlands full enforcement, EU Platform Work Directive, Chile 42-hour law, Germany Blue Card changes.
KWBench: Measuring Unprompted Problem Recognition in Knowledge Work
arXiv:2604.15760v1 Announce Type: new Abstract: We introduce the first version of KWBench (Knowledge Work Bench), a benchmark for unprompted problem recognition in large language models: can an LLM identify a professional scenario before attempting to solve it. Existing frontier benchmarks have saturated, and most knowledge-work evaluations to date reduce to extraction or task completion against
How fears of AI disruption are reshaping career choices and increasing interest in graduate education among youth - The Times of India
News News: As artificial intelligence reshapes hiring patterns and disrupts traditional entry-level roles, a growing number of young graduates are rethinking the.
Punjab Leads AI Education Revolution with Google and Intel Workshops, PSEB Embeds AI in Curriculum
Punjab's education system is set to integrate AI and robotics into its curriculum, starting this academic session, as announced at a recent conference.
India Trains 2,500 Artisans in AI for Inclusive Digital Transformation Under PM Vishwakarma Scheme
India's MSME Ministry has trained over 2,500 artisans in AI tools under the PM Vishwakarma Scheme, aiming to enhance digital skills and market competitiveness.
Technology & Infrastructure
AI Nuclear Power Developer Fermi Slumps After Abrupt Exit of CEO
Fermi Inc., a developer of nuclear power for AI data centers, slumped after the sudden departure of co-founder and Chief Executive Officer Toby Neugebauer along with the company’s chief financial officer.
Growing AI power slurpage prompts MPs to examine low-energy computing
Committee launches inquiry into emerging chip designs to curb datacenter energy use MPs are probing whether radically different, low-energy chip designs can stop AI from turning the UK's power grid into a bottleneck.…
AirTrunk to Acquire Lumina CloudInfra to Expand in India
AirTrunk, a data center operator backed by Blackstone Inc., is buying Lumina CloudInfra to expand in India.
Risk Management: Financial Institutions
The soaring level of US government debt; how AI is increasing the speed and depth of cyber attacks; and geopolitical shocks have highlighted the need for diversity in cloud providers
AI is reshaping Britain's datacenter map away from London
Bit barns need to worry more about space, access to grid – overstuffed center no longer a must, say experts UK AI datacenter capacity could migrate away from London as power shortages, planning constraints and reduced reliance on low-latency connections to financial firms make other locations more attractive.…
The next great AI breakthrough isn’t a model—It’s infrastructure
The most consequential opportunity does not lie in deploying digital public infrastructure or AI in isolation. It lies in their convergence.
The RAM shortage could last years
Analysis of the ongoing RAM supply chain issues and projections for a long-term shortage.
Strengthening the future of AI infrastructure
As AI demand grows, power optimization is critical to scaling AI infrastructure. Arm's high-performance, power-efficient compute platform enables partners to develop competitive chips and hardware.
AI Computing Power Industry Analysis: Navigating the Supply Crunch, Chip Innovation, and Infrastructure Gold Rush
Press release - QY Research Inc. - AI Computing Power Industry Analysis: Navigating the Supply Crunch, Chip Innovation, and Infrastructure Gold Rush - published on openPR.com
Organizers Reveal Strategies to Successfully Resist a Data Center - El-Balad.com
Communities across the United States are increasingly resisting the rapid expansion of data centers that support artificial intelligence (AI) technologies. As the demand for these facilities continues to grow, local activists are mobilizing to combat their adverse effects, including water and ...
Global AI Data Center Infrastructure for High-Performance Computing Market to Reach US$ 96.44 Billion by 2032 as the United States Leads at Scale and Japan Strengthens the Next Phase of Efficient AI Infrastructure
April 19 2026 Global Reports Store announced the release of its latest study on the AI Data Center Infrastructure for High Performance Computing Market presenting a market that is rapidly becoming one of the most critical investment layers in the ...
Reuters AI News | Latest Headlines and Developments | Reuters
Explore the latest artificial intelligence news with Reuters - from AI breakthroughs and technology trends to regulation, ethics, business and global impact.
Training Data and AI
OpenAI head of science Kevin Weil and head of Sora Bill Peebles are leaving the company, as is Srinivas Narayanan, who had been CTO of the company's B2B unit. The National Security Agency is using Anthropic's most powerful model yet, Mythos Preview, despite top officials at the Department of Defense insisting the company is a 'supply chain risk.'
Discover and Prove: An Open-source Agentic Framework for Hard Mode Automated Theorem Proving in Lean 4
arXiv:2604.15839v1 Announce Type: new Abstract: Most ATP benchmarks embed the final answer within the formal statement -- a convention we call "Easy Mode" -- a design that simplifies the task relative to what human competitors face and may lead to optimistic estimates of model capability. We call the stricter, more realistic setting "Hard Mode": the system must independently discover the answer before constructing a formal proof. To enable Hard Mode research, we make two contributions. First, we release MiniF2F-Hard and FIMO-Hard, expert-reannotated Hard Mode variants of two widely-used ATP benchmarks. Second, we introduce Discover And Prove (DAP), an agentic framework that uses LLM natural-language reasoning with explicit self-reflection to discover answers, then rewrites Hard Mode statements into Easy Mode ones for existing ATP provers. DAP sets the state of the art: on CombiBench it raises solved problems from 7 (previous SOTA, Pass@16) to 10; on PutnamBench it is the first system to formally prove 36 theorems in Hard Mode -- while simultaneously revealing that state-of-the-art LLMs exceed 80% answer accuracy on the same problems where formal provers manage under 10%, exposing a substantial gap that Hard Mode benchmarks are uniquely suited to measure.
Bilevel Optimization of Agent Skills via Monte Carlo Tree Search
arXiv:2604.15709v1 Announce Type: new Abstract: Agent \texttt{skills} are structured collections of instructions, tools, and supporting resources that help large language model (LLM) agents perform particular classes of tasks. Empirical evidence shows that the design of \texttt{skills} can materially affect agent task performance, yet systematically optimizing \texttt{skills} remains challenging. Since a \texttt{skill} comprises instructions, tools, and supporting resources in a structured way, optimizing it requires jointly determining both the structure of these components and the content each component contains. This gives rise to a complex decision space with strong interdependence across structure and components. We therefore represent these two coupled decisions as \texttt{skill} structure and component content, and formulate \texttt{skill} optimization as a bilevel optimization problem. We propose a bilevel optimization framework in which an outer loop employs Monte Carlo Tree Search to determine the \texttt{skill} structure, while an inner loop refines the component content within the structure selected by the outer loop. In both loops, we employ LLMs to assist the optimization procedure. We evaluate the proposed framework on an open-source Operations Research Question Answering dataset, and the experimental results suggest that the bilevel optimization framework improves the performance of the agents with the optimized \texttt{skill}.
Experience Compression Spectrum: Unifying Memory, Skills, and Rules in LLM Agents
arXiv:2604.15877v1 Announce Type: new Abstract: As LLM agents scale to long-horizon, multi-session deployments, efficiently managing accumulated experience becomes a critical bottleneck. Agent memory systems and agent skill discovery both address this challenge -- extracting reusable knowledge from interaction traces -- yet a citation analysis of 1,136 references across 22 primary papers reveals a cross-community citation rate below 1%. We propose the \emph{Experience Compression Spectrum}, a unifying framework that positions memory, skills, and rules as points along a single axis of increasing compression (5--20$\times$ for episodic memory, 50--500$\times$ for procedural skills, 1,000$\times$+ for declarative rules), directly reducing context consumption, retrieval latency, and compute overhead. Mapping 20+ systems onto this spectrum reveals that every system operates at a fixed, predetermined compression level -- none supports adaptive cross-level compression, a gap we term the \emph{missing diagonal}. We further show that specialization alone is insufficient -- both communities independently solve shared sub-problems without exchanging solutions -- that evaluation methods are tightly coupled to compression levels, that transferability increases with compression at the cost of specificity, and that knowledge lifecycle management remains largely neglected. We articulate open problems and design principles for scalable, full-spectrum agent learning systems.
The World Leaks the Future: Harness Evolution for Future Prediction Agents
arXiv:2604.15719v1 Announce Type: new Abstract: Many consequential decisions must be made before the relevant outcome is known. Such problems are commonly framed as \emph{future prediction}, where an LLM agent must form a prediction for an unresolved question using only the public information available at the prediction time. The setting is difficult because public evidence evolves while useful supervision arrives only after the question is resolved, so most existing approaches still improve mainly from final outcomes. Yet final outcomes are too coarse to guide earlier factor tracking, evidence gathering and interpretation, or uncertainty handling. When the same unresolved question is revisited over time, temporal contrasts between earlier and later predictions can expose omissions in the earlier prediction process; we call this signal \emph{internal feedback}. We introduce \emph{Milkyway}, a self-evolving agent system that keeps the base model fixed and instead updates a persistent \emph{future prediction harness} for factor tracking, evidence gathering and interpretation, and uncertainty handling. Across repeated predictions on the same unresolved question, \emph{Milkyway} extracts internal feedback and writes reusable guidance back into the harness, so later predictions on that question can improve before the outcome is known. After the question is resolved, the final outcome provides a \emph{retrospective check} before the updated harness is carried forward to subsequent questions. On FutureX and FutureWorld, Milkyway achieves the best overall score among the compared methods, improving FutureX from 44.07 to 60.90 and FutureWorld from 62.22 to 77.96.
Structured Abductive-Deductive-Inductive Reasoning for LLMs via Algebraic Invariants
arXiv:2604.15727v1 Announce Type: new Abstract: Large language models exhibit systematic limitations in structured logical reasoning: they conflate hypothesis generation with verification, cannot distinguish conjecture from validated knowledge, and allow weak reasoning steps to propagate unchecked through inference chains. We present a symbolic reasoning scaffold that operationalizes Peirce's tripartite inference -- abduction, deduction, and induction -- as an explicit protocol for LLM-assisted reasoning. The framework enforces logical consistency through five algebraic invariants (the Gamma Quintet), the strongest of which -- the Weakest Link bound -- ensures that no conclusion in a reasoning chain can exceed the reliability of its least-supported premise. This principle, independently grounded as weakest link resolution in possibilistic logic and empirically validated for chain-of-thought reasoning, prevents logical inconsistencies from accumulating across multi-step inference. We verify all invariants through a property-based testing suite of 100 properties and 16 fuzz tests over 10^5+ generated cases, providing a verified reference implementation of the invariants suitable as a foundation for future reasoning benchmarks.
LACE: Lattice Attention for Cross-thread Exploration
arXiv:2604.15529v1 Announce Type: new Abstract: Current large language models reason in isolation. Although it is common to sample multiple reasoning paths in parallel, these trajectories do not interact, and often fail in the same redundant ways. We introduce LACE, a framework that transforms reasoning from a collection of independent trials into a coordinated, parallel process. By repurposing the model architecture to enable cross-thread attention, LACE allows concurrent reasoning paths to share intermediate insights and correct one another during inference. A central challenge is the absence of natural training data that exhibits such collaborative behavior. We address this gap with a synthetic data pipeline that explicitly teaches models to communicate and error-correct across threads. Experiments show that this unified exploration substantially outperforms standard parallel search, improving reasoning accuracy by over 7 points. Our results suggest that large language models can be more effective when parallel reasoning paths are allowed to interact.
LLM Reasoning Is Latent, Not the Chain of Thought
arXiv:2604.15726v1 Announce Type: new Abstract: This position paper argues that large language model (LLM) reasoning should be studied as latent-state trajectory formation rather than as faithful surface chain-of-thought (CoT). This matters because claims about faithfulness, interpretability, reasoning benchmarks, and inference-time intervention all depend on what the field takes the primary object of reasoning to be. We ask what that object should be once three often-confounded factors are separated and formalize three competing hypotheses: H1, reasoning is primarily mediated by latent-state trajectories; H2, reasoning is primarily mediated by explicit surface CoT; and H0, most apparent reasoning gains are better explained by generic serial compute than by any privileged representational object. Reorganizing recent empirical, mechanistic, and survey work under this framework, and adding compute-audited worked exemplars that factorize surface traces, latent interventions, and matched budget expansions, we find that current evidence most strongly supports H1 as a default working hypothesis rather than as a task-independent verdict. We therefore make two recommendations: the field should treat latent-state dynamics as the default object of study for LLM reasoning, and it should evaluate reasoning with designs that explicitly disentangle surface traces, latent states, and serial compute.
A single AI model reshaped global financial governance, cracked a decades-old cancer target, and forced a White House reversal
The UK AI Security Institute confirmed that Mythos completed a 32-step autonomous cyberattack simulation - tasks that would “normally take human professionals days” - succeeding in three of ten attempts, the first AI model to do so.
Agentic Engineering: How Swarms of AI Agents Are Redefining Software Engineering
LangChain explores multi-agent software development patterns, focusing on orchestration and governance within agentic systems.
Adoption & Impact
Struggle Premium : How Human Effort and Imperfection Drive Perceived Value in the Age of AI
arXiv:2604.15324v1 Announce Type: cross Abstract: As AI enters creative practice, audiences face growing uncertainty in judging authenticity and value. This study examines the Struggle Premium, the added value attributed to perceived human effort, by analyzing how visible effort cues influence evaluations of human- and AI-generated creative works. We surveyed 70 university students, focusing on process videos, time documentation, written explanations, and imperfections. Process-oriented cues, especially videos and time spent, most strongly shaped authenticity and value judgments, while imperfections had limited impact. Participants showed a clear preference for human-made works, with 72.9% willing to pay more. Notably, effort cues also improved perceptions of AI-generated content, suggesting that process transparency can partially bridge authenticity gaps. These findings extend the effort heuristic to algorithmic creativity and inform the design of transparent human-AI creative systems.
Decoding ROI from AI | PwC
The strength of connection between corporate strategy and AI deployment. Does the organisation have a prioritised AI road map? Is every use case linked to a clear business objective? Is business impact tracked? And is someone accountable for every critical AI outcome? Watch Daria Vlasova, AI Strategy & Go-to-Market lead, PwC UK, explain how the AI leaders root their AI planning in their strategic growth priorities. ... For many companies, AI adoption ...
Improving Recycling Accuracy across UK Local Authorities: A Prototype for Citizen Engagement
arXiv:2604.15345v1 Announce Type: cross Abstract: Despite public motivation to recycle, significant barriers hinder effective household recycling in the UK. Decentralised local authority waste management creates citizen confusion and "wishcycling" (disposing of non-recyclable items in recycling bins). The recent Simpler Recycling Policy further complicates this landscape by mandating new identification, sorting, and cleaning requirements that will require citizen guidance to ensure they understand how these will impact their recycling practices. This mixed methods study (surveys n=50, expert interviews, design activities) used the Value Proposition Canvas to identify citizen pain points: confusion about logos, logistical constraints, and information gaps about local requirements. We then developed an interactive prototype application providing location-specific guidance, visual sorting aids, and material-specific information to address these painpoints. Focus group evaluation showed the prototype improved recycling accuracy by 60 percent, with marked improvements in packaging assessment. Technology-enabled solutions grounded in user-centred design can measurably improve recycling behaviours and reduce contamination. However, such solutions are most effective when complementing (rather than substituting for) systemic improvements in local authority communication and service design.
The State of AI Adoption in the Enterprise [Q1 2026 Review] | Rick's Cafe AI
You’ve seen the headline: “95% of enterprise AI pilots fail.”... The 95% figure measures one thing: whether an AI pilot produced rapid P&L impact within six months. Not productivity. Not cost savings. Not efficiency gains. And it mostly measured pilots in sales and marketing — the ...
UK firms are grappling with mismatched AI productivity gains – employees are more efficient, but business performance is stagnating | IT Pro
AI productivity gains are largely "localized" to individual workers, according to Accenture, with IT leaders failing to scale adoption across the business.
Tesla brings its robotaxi service to Dallas and Houston
Tesla expands its autonomous robotaxi service to the Texas cities of Dallas and Houston.
Google's Gemini AI Surpasses 1 Billion Monthly Visits, Revolutionizing Digital Interaction and Productivity
Google's Gemini AI now garners over 1 billion monthly visits, significantly enhancing tasks like coding, writing, and research across its ecosystem.
Inside San Francisco's AI-run store
Andon Market is an Anthropic-backed retail experiment testing whether an AI agent named Luna can effectively manage a business, from inventory to customer engagement.
GIST: Multimodal Knowledge Extraction and Spatial Grounding via Intelligent Semantic Topology
arXiv:2604.15495v1 Announce Type: new Abstract: Navigating complex, densely packed environments like retail stores, warehouses, and hospitals poses a significant spatial grounding challenge for humans and embodied AI. In these spaces, dense visual features quickly become stale given the quasi-static nature of items, and long-tail semantic distributions challenge traditional computer vision. While Vision-Language Models (VLMs) help assistive systems navigate semantically-rich spaces, they still struggle with spatial grounding in cluttered environments. We present GIST (Grounded Intelligent Semantic Topology), a multimodal knowledge extraction pipeline that transforms a consumer-grade mobile point cloud into a semantically annotated navigation topology. Our architecture distills the scene into a 2D occupancy map, extracts its topological layout, and overlays a lightweight semantic layer via intelligent keyframe and semantic selection. We demonstrate the versatility of this structured spatial knowledge through critical downstream Human-AI interaction tasks: (1) an intent-driven Semantic Search engine that actively infers categorical alternatives and zones when exact matches fail; (2) a one-shot Semantic Localizer achieving a 1.04 m top-5 mean translation error; (3) a Zone Classification module that segments the walkable floor plan into high-level semantic regions; and (4) a Visually-Grounded Instruction Generator that synthesizes optimal paths into egocentric, landmark-rich natural language routing. In multi-criteria LLM evaluations, GIST outperforms sequence-based instruction generation baselines. Finally, an in-situ formative evaluation (N=5) yields an 80% navigation success rate relying solely on verbal cues, validating the system's capacity for universal design.
Leaders Can No Longer Fake an AI Strategy - CEOWORLD magazine
The era of corporate AI theater is ending. A recent Wharton Human-AI Research and GBK Collective study finds that 82% of enterprise leaders use generative AI at least weekly and 46% use it daily. That is no longer experimentation. Yet McKinsey’s 2025 state of AI survey shows a stubborn gap ...
Business AI adoption surges as NAB urges Australia to ‘get on the bus’
The AI chief of National Australia Bank has urged Australia to “get on the bus” and adopt the technology or risk being left behind, as new research shows about half of the country’s small businesses are using the technology to accelerate growth.
AI Consumer Devices Steal the Show at China's CICPE Expo, with Rokid's Glasses Leading the Charge
At the China International Consumer Products Expo, AI-enabled consumer devices, notably Rokid's AI glasses, are a major highlight, showcasing advancements in integrating AI into daily life.
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Banks are seeking to use AI as a tool for both protection and competition
Financial institutions must move from reactive defence to predictive technology to combat the criminals
Polarization by Default: Auditing Recommendation Bias in LLM-Based Content Curation
arXiv:2604.15937v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly deployed to curate and rank human-created content, yet the nature and structure of their biases in these tasks remains poorly understood: which biases are robust across providers and platforms, and which can be mitigated through prompt design. We present a controlled simulation study mapping content selection biases across three major LLM providers (OpenAI, Anthropic, Google) on real social media datasets from Twitter/X, Bluesky, and Reddit, using six prompting strategies (\textit{general}, \textit{popular}, \textit{engaging}, \textit{informative}, \textit{controversial}, \textit{neutral}). Through 540,000 simulated top-10 selections from pools of 100 posts across 54 experimental conditions, we find that biases differ substantially in how structural and how prompt-sensitive they are. Polarization is amplified across all configurations, toxicity handling shows a strong inversion between engagement- and information-focused prompts, and sentiment biases are predominantly negative. Provider comparisons reveal distinct trade-offs: GPT-4o Mini shows the most consistent behavior across prompts; Claude and Gemini exhibit high adaptivity in toxicity handling; Gemini shows the strongest negative sentiment preference. On Twitter/X, where author demographics can be inferred from profile bios, political leaning bias is the clearest demographic signal: left-leaning authors are systematically over-represented despite right-leaning authors forming the pool plurality in the dataset, and this pattern largely persists across prompts.
DeepER-Med: Advancing Deep Evidence-Based Research in Medicine Through Agentic AI
arXiv:2604.15456v1 Announce Type: new Abstract: Trustworthiness and transparency are essential for the clinical adoption of artificial intelligence (AI) in healthcare and biomedical research. Recent deep research systems aim to accelerate evidence-grounded scientific discovery by integrating AI agents with multi-hop information retrieval, reasoning, and synthesis. However, most existing systems lack explicit and inspectable criteria for evidence appraisal, creating a risk of compounding errors and making it difficult for researchers and clinicians to assess the reliability of their outputs. In parallel, current benchmarking approaches rarely evaluate performance on complex, real-world medical questions. Here, we introduce DeepER-Med, a Deep Evidence-based Research framework for Medicine with an agentic AI system. DeepER-Med frames deep medical research as an explicit and inspectable workflow of evidence-based generation, consisting of three modules: research planning, agentic collaboration, and evidence synthesis. To support realistic evaluation, we also present DeepER-MedQA, an evidence-grounded dataset comprising 100 expert-level research questions derived from authentic medical research scenarios and curated by a multidisciplinary panel of 11 biomedical experts. Expert manual evaluation demonstrates that DeepER-Med consistently outperforms widely used production-grade platforms across multiple criteria, including the generation of novel scientific insights. We further demonstrate the practical utility of DeepER-Med through eight real-world clinical cases. Human clinician assessment indicates that DeepER-Med's conclusions align with clinical recommendations in seven cases, highlighting its potential for medical research and decision support.
Lunit Unveils AI Breakthroughs in Oncology at AACR 2026, Boosting Biomarker Precision and Treatment Strategies
Lunit unveiled six groundbreaking AI-driven studies at AACR 2026, focusing on enhancing cancer treatment decision-making through AI-powered biomarkers and tumor microenvironment analysis.
Agentic AI and payments: implications of autonomous economic agents. | The AI Journal
The integration of artificial intelligence (AI) into financial services is progressing beyond advisory tools and assistants toward autonomous economic actors
Towngas, Tencent partner on cloud, AI services, to co-build digital platforms for energy industry - Telecompaper
The Hong Kong and China Gas Company (Towngas) and Tencent have signed a strategic partnership agreement in Hong Kong. The two companies will collaborate extensively on unified cloud resource management, digital platform development, large AI models and applications, customer engagement enhancement, ...
LLMbench: A Comparative Close Reading Workbench for Large Language Models
arXiv:2604.15508v1 Announce Type: new Abstract: LLMbench is a browser-based workbench for the comparative close reading of large language model (LLM) outputs. Where existing tools for LLM comparison, such as Google PAIR's LLM Comparator are engineered for quantitative evaluation and user-rating metrics, LLMbench is oriented towards the hermeneutic practices of the digital humanities. Two model responses to the same prompt are side by side in annotatable panels with four analytical overlays (Probabilities for token-level log-probability inspection, Differences for word-level diff across the two panels, Tone for Hyland-style metadiscourse analysis, and Structure for sentence-level parsing with discourse connective highlighting), alongside five analytical modes, Stochastic Variation, Temperature Gradient, Prompt Sensitivity, Token Probabilities, and Cross-Model Divergence, that make the probabilistic structure of generated text legible at the token level. The tool treats the generated text as a research object in its own right from a probability distribution, a text that could have been otherwise, and provides visualisations including continuous heatmaps, entropy sparklines, pixel maps, and three-dimensional probability terrains, that show the counterfactual history from which each word emerged. This paper describes the tool's architecture, its six modes, and its design rationale, and argues that log-probability data, currently underused in humanistic and social-scientific readings of AI, is an important resource for a critical studies of generative AI models.
Agentic AI Reduces Web Page Assembly Time
Agentic AI can shrink web page assembly from hours to minutes by coordinating content planning, layout selection, and publishing inside a marketing workflow.
Can LLMs Help Decentralized Dispute Arbitration? A Case Study of UMA-Resolved Markets on Polymarket
arXiv:2604.15674v1 Announce Type: new Abstract: Web3 prediction markets, exemplified by Polymarket, have gained prominence for leveraging collective intelligence to forecast a wide range of social, political, and sports events. However, among the thousands of prediction market events, consensus disputes still arise due to imperfections in market mechanisms. On Polymarket alone, the trading volume involving disputed events has reached $972,370,804.71, underscoring the critical need for objective and efficient dispute resolution. In this study, we introduce large language models (LLMs) to: (1) evaluate whether web-enabled LLMs can reproduce the decision quality of UMA's on-chain voting process once a dispute has been raised, and (2) predict, based on event rules, which market events are likely to face future disputes before they occur. Our findings show that LLMs are unable to reliably predict which events will become disputed in advance; however, once a dispute is initiated, web-enabled LLMs achieve 89.58% agreement with UMA's final resolutions and demonstrate strong stability.
Ukraine’s drone pilots hit Russian targets from 500km away
New internet-based guidance system allows operation of unmanned aerial vehicles far from battlefield
Driving Assistance System for Ambulances to Minimise the Vibrations in Patient Cabin
arXiv:2604.16047v1 Announce Type: cross Abstract: The ambulance service is the main transport for diseased or injured people which suffers the same acceleration forces as regular vehicles. These accelerations, caused by the movement of the vehicle, impact the performance of tasks executed by sanitary personnel, which can affect patient survival or recovery time. In this paper, we have trained, validated, and tested a system to assess driving in ambulance services. The proposed system is composed of a sensor node which measures the vehicle vibrations using an accelerometer. It also includes a GPS sensor, a battery, a display, and a speaker. When two possible routes reach the same destination point, the system compares the two routes based on previously classified data and calculates an index and a score. Thus, the index balances the possible routes in terms of time to reach the destination and the vibrations suffered in the patient cabin to recommend the route that minimises those vibrations. Three datasets are used to train, validate, and test the system. Based on an Artificial Neural network (ANN), the classification model is trained with tagged data classified as low, medium, and high vibrations, and 97% accuracy is achieved. Then, the obtained model is validated using data from three routes of another region. Finally, the system is tested in two new scenarios with two possible routes to reach the destination. The results indicate that the route with less vibration is preferred when there are low time differences (less than 6%) between the two possible routes. Nonetheless, with the current weighting factors, the shortest route is preferred when time differences between routes are higher than 20%, regardless of the higher vibrations in the shortest route.
Geopolitics
Anthropic's relationship with the Trump administration seems to be thawing
Signs suggest a warming of relations between AI company Anthropic and the Trump administration.
Volatile geopolitics slows GCC in March - The Economic Times
Global geopolitical tensions are impacting new technology center openings in India. While the number of new centers slowed, existing ones are expanding. Experts predict continued growth in greenfield centers if uncertainties ease. The GCC ecosystem is steadily rising, driven by new capabilities ...
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