Tue 2 June 2026
Daily Brief — Curated and contextualised by Best Practice AI
Anthropic Files for IPO, Alphabet Sells $80bn in Stock, and Ohio Halts Tax Breaks
TL;DR Generative AI's impact on sales productivity is being tested in large-scale online retail experiments. Anthropic has filed for an IPO, aiming to capitalize on its rapid growth. Alphabet plans to sell $80 billion in stock to fund its AI initiatives. Ohio has paused data center tax breaks due to financial strain. Bain & Co. reports that automation cost savings are falling short of expectations.
The stories that matter most
Selected and contextualised by the Best Practice AI team
Generative AI and Sales Productivity: Field Experiments in Online Retail
arXiv:2510.12049v4 Announce Type: replace Abstract: We quantify the short-term impact of Generative Artificial Intelligence (GenAI) on sales performance through a series of large-scale randomized field experiments involving millions of users and products at a leading cross-border online retail platform. Over 2023-2024, the platform integrated GenAI into seven consumer-facing business workflows spanning customer service, consumer-product matching, advertising, and seller services. We find that GenAI adoption increases sales in most workflows, with effects ranging from no detectable impact to $16.3\%$, depending on GenAI's marginal contribution relative to baseline firm practices. Across the four GenAI applications with positive sales effects, the implied annual incremental value is roughly $\$5-$an economically meaningful impact given the retailer's scale and the early stage of GenAI adoption. The gains operate primarily through higher conversion rates rather than larger cart values, consistent with GenAI improving the shopping experience by reducing search, information, communication, and personalization frictions. Importantly, these effects are not associated with worse post-purchase outcomes, as product return rates and customer ratings do not deteriorate. Finally, we document substantial demand-side heterogeneity, with larger gains for less experienced consumers. Our findings provide novel, large-scale causal evidence on how GenAI shapes sales productivity in online retail, highlighting both its immediate value and broader potential.
AI-driven labor displacement risks to remain low in near term, Bridgewater says | Reuters
Risks of widespread job losses from AI are expected to remain limited this year, according to Bridgewater Associates, with constraints on computing capacity and a resilient economy blunting the technology's near-term impact on employment.
Strategic Preemption Under Shared Catastrophic Risk: The Suicide Region and the Race to Artificial General Intelligence
arXiv:2512.07526v3 Announce Type: replace-cross Abstract: We analyze a continuous-time preemption game with shared catastrophic externalities. When the cost of catastrophe is embedded in both players' payoffs, the risk term cancels out in the equilibrium indifference condition. This creates a "suicide region" where competitive pressures force rational agents to deploy despite negative risk-adjusted net present values. We apply this framework to the race for artificial general intelligence (AGI). We show that this suicide region widens as the cost of systemic ruin grows: higher catastrophic risk does not deter the race but instead enlarges the set of conditions under which rational actors deploy despite negative social value. We characterize the resulting welfare distortion against a social planner's benchmark and demonstrate how two complementary mechanisms - private liability and prize-sharing - can close the suicide region. Private liability raises the cost of unsafe deployment while prize-sharing reduces the strategic imperative to deploy first. "Warning shots" (sub-existential disasters) will fail to deter AGI acceleration, as the winner-takes-all nature of the race remains intact.
Ohio hits pause on datacenter tax breaks draining its coffers
Buckeye State found it had inadvertently joined the billion dollar losers' club
TransResAI: A Compound AI System for Coastal Transportation Resilience
arXiv:2606.00042v1 Announce Type: new Abstract: Coastal flooding increasingly threatens transportation infrastructure, yet the analytical tools needed for resilience management remain difficult for many non-specialist practitioners to use. This study presents TransResAI, a compound AI system that supports analysis of flood-aware transportation resilience via natural-language interactions. The system integrates a locally deployable Large Language Model (LLM) with modules for task decomposition, secure code generation, geospatial analysis, retrieval-augmented generation, and interactive map rendering. TransResAI links MATSim flood-scenario simulation outputs, OpenStreetMap-derived flood-risk networks, equity-focused demographic indicators, and regional documents in Hampton Roads, Virginia. A structured user study with domain experts demonstrated that TransResAI reduced task completion time by 80-88% relative to conventional GIS workflows, compressing analytical tasks from a mean of 197.1 seconds to 29.7 seconds and visualization tasks from 364.0 seconds to 46.1 seconds, while maintaining mean accuracy of 4.60/5.00 and task completion rates exceeding 94%. These findings demonstrate that compound AI architectures bridge the gap between general-purpose language models and specialized domain knowledge, as well as the quantitative rigor required for infrastructure resilience, providing transportation agencies and communities with faster, more accessible analytical tools for decision-making under growing climate uncertainty.
Will the IT consulting share price rout ever end?
Accenture made a fortune from previous tech revolutions but investors think AI could kill it, not make it stronger
Grokers: Bottom-Up Inductive Comprehension and Write-Time Intelligence over Typed Knowledge Graphs
arXiv:2606.00050v1 Announce Type: new Abstract: We present Grokers, an architecture for building persistent, structured comprehension of typed knowledge graphs through bottom-up inductive traversal of dependency subgraphs. Unlike retrieval-augmented generation (RAG), which pays full comprehension cost at every query, Grokers pushes intelligence to write time: autonomous Groker agents analyze nodes in a typed stream graph, extract structured attributes via governed language model (LM) calls, and inductively compose that understanding upward through dependency relations, writing enriched typed attributes that serve all future queries at zero additional LM cost. We prove three formal properties: (1) the Byte-Identity Theorem, establishing that context blocks assembled from a transactionally-maintained denormalization index are byte-identical across LM turns between semantic changes, enabling KV-cache hit rates approaching 100%; (2) the Accumulation Monotonicity Theorem, establishing that the fraction of interactions resolved without LM calls is non-decreasing in the number of completed interactions under a governed wisdom library growth protocol; and (3) the Dual-Traversal Ordering Theorem, establishing that top-down generation and bottom-up comprehension are the unique correct traversal orderings for their respective tasks over a dependency DAG, and that their composition closes into a complete generation-comprehension cycle. We further present a deterministic alternative to embedding-based semantic search, with a synonym caching protocol whose LM fallback rate converges to zero for finite-vocabulary domains. A reference implementation is provided in the open-source Qbix / Safebox / Safebots stack.
AI Savings Misses 'Should Be Making Executives Uncomfortable,' Bain Says
Cost savings from automation are broadly falling short of projections, according to a new Bain & Co. global survey of large companies. The missed
Economics & Markets
🔥 We checked. Again. Still no bubble.
A customer-led boom with a few fraying edges.
I won a Pulitzer for explaining the Great Depression. The AI spending boom terrifies me
The historian behind Lords of Finance has a warning for Silicon Valley: He's seen these numbers before — and they didn't end well.
What Exactly Is Agentic AI, and Why Are Some Stocks Blowing Up Because of It? | Investing.com
Rather than simply responding to ... external systems, coordinate with other tools, and then execute actions autonomously. The difference isn’t just technical. It’s the difference between an AI that tells you what to do and one that goes and does it itself. That shift, from AI as a tool to AI as an actor, has far-reaching applications across multiple industries, and this is what has the market so excited. The first and most dramatic investment implication of agentic AI is its impact on infrastructure ...
AI-Driven Search Boom Boosts Alphabet, Reddit, Meta; Meta Seen as Undervalued
Alphabet's AI-driven search expansion is boosting ad revenue, while Meta is positioned as potentially undervalued in the competitive tech landscape.
3 Market Predictions For June | Seeking Alpha
A coming surge in IPOs, including SpaceX at a $1.75–$2T valuation, suggests speculative excess and risk for passive index investors. I expect the market to peak for 2026 in June, with GDPNow’s Q2 growth forecast likely to be slashed toward 2% amid consumer weakness. The AI narrative could ...
AI Financing Trends Highlighted at Goldman Sachs Conference (SMCI)
On June 01, 2026, at the Goldman Sachs annual leveraged finance and credit conference in Dana Point, California, discussions centered around the increasing dema
Sridhar Vembu Warns of AI Bubble: What His Concerns Mean for the Future of Artificial Intelligence - Best Stock Market Blogs & Investment Insights | Equentis
The promise of improved productivity has encouraged widespread adoption. Public markets have also embraced the AI theme. Companies associated with AI have experienced substantial increases in market value as investors anticipate future growth opportunities.
Amundi Is Diversifying Risk Via Commodity Currencies, Gold
John O’Toole, head of CIO solutions at Amundi, discusses risk diversification strategies as the war in the Middle East continues and the artificial intelligence trade dominates equity markets. "We've seen opportunities in high-yielding commodity currencies for example," O’Toole tells Bloomberg Television. "We have had exposure to gold, and continue to have exposure to gold," he adds. (Source: Bloomberg)
Sam Altman Is Backing a Startup That's Building Software for Robots and Cars - Business Insider
A former Tesla designer's startup has won investment from Sam Altman and other top investors as money floods into physical AI.
AI Weekly For Leaders – June 1, 2026 - by Cezary
This digest summarises developments from the last week (to June 1, 2026) across global, Canadian, UK, Australian and European contexts, offering concise context and strategic insights. AI‑driven market dynamics in Europe – European equities tied to the AI supply chain and infrastructure continued to rally. Companies involved in chip manufacturing and data‑centre infrastructure outperformed the broader market despite lingering geopolitical ...
The U.S. and Europe feared the Iran conflict would curtail the Gulf’s appetite for global investments. The opposite is true
The five biggest spenders–split across Saudi Arabia, the UAE and Qatar–collectively spent almost $26 billion during March, April and May.
Differing Roles of Leisure and Productivity in GDP - A Machine Learning based comparative analysis of Germany and USA
arXiv:2606.01234v1 Announce Type: new Abstract: The GDP of a country is modelled as the relative interaction between two agents - working hours, reflecting the social choice of a population, and Total Factor Productivity, reflecting the collective investment in productivity enhancers. It is shown that a Random Forest model can accu- rately predict the GDP from these two factors. The differences in the choices made by Germany and USA are analysed though Gini importance, SHAP plots and partial dependency. It is shown that the differences in the social structure of the countries are reflected in the relative contribution of working hours and productivity to the GDP.
Recession Detection in Japan using Labor Market Data
arXiv:2606.00948v1 Announce Type: new Abstract: Recession indicators are often viewed as U.S. specific, raising the question of whether labor market-based rules such as the Sahm Rule and the Michez Rule can reliably detect recessions in other countries. To answer this, we evaluate whether such rules can be adapted to Japan by calibrating thresholds and smoothing parameters to Japanese labor market data. We construct a large set of 95,832 recession indicators combining unemployment and vacancy data. The selected classifiers are statistically perfect as they identify all 11 historical recessions in the 1970-2021 training period without generating any false positives. Among these, 193 classifiers lie on the anticipation-precision frontier. Restricting attention to the high-precision segment yields six classifiers with a standard deviation of detection errors below 3 months. The selected classifier ensemble signals recessions, on average, 0.06 months after their true onset. Overall, these findings suggest that slack-based labor market rules provide a general framework for improving real-time recession detection across countries.
Reuters Reuters | Breaking International News & Views
The Iran energy story may be masking a bigger inflation worry. The AI boom is building a head of steam in prices under the surface, and it's a boom that will likely outlast any hiatus in the Gulf.
AI to drive up UK youth unemployment, as Alphabet raises $80bn for spending splurge – business live
Rolling coverage of the latest economic and financial news Anthropic confidentially files for initial public offering on US stock market Inflation across the eurozone has risen to its highest level since September 2023, adding to the pressure on the European Central Bank to lift interest rates. Eurozone consumer price inflation hit 3.2% in May, a new estimate from statistics body Eurostat shows, up from 3% in April. Eurozone inflation rose to 3.2% in May as pressure builds on the European Central Bank to act now to combat rising prices. Despite efforts to control it, underlying price pressures remain strong, with service inflation and wage growth persisting, businesses passing on costs, and global instability driving up energy and transport costs. These factors further strain supply chains across Europe. With this uptick in inflation, the ECB is increasingly likely to raise interest rates by 0.25 percentage points next week. This marks a shift from earlier expectations that the ECB might ease policy later this year to reignite the sluggish Eurozone economy. “Continued resilience in mortgage lending and housing market activity in April suggests the economic fallout from conflict in the Middle East has not yet hit household borrowing as hard as some feared. Mortgage approvals rose to a 15-month high of 65,945 in April, up from March’s 63,979, while gross mortgage lending, was the second-highest level since March 2025. “Household borrowing is proving surprisingly resilient in the face of geopolitical uncertainty, higher inflation risks and tighter financial conditions. The housing market is far from booming, but it is holding up better than the wider economic backdrop might suggest. Continue reading...
Import AI 459: AI oversight is difficult; scaling laws for protein folding models; and pricing the extinction risk of AI systems
By comparison, where the majority of AI ’s economic impact is taking place is in AI inference - the usage of AI ’s systems - but there are confounding factors here as it relates to GDP measurement: “Nominal AI revenues grow only moderately because per-unit prices for any given level of AI capability fall almost as fast as quality-adjusted output rises,” they write.
ASEAN concludes negotiations on digital-economy deal
ASEAN officials have concluded negotiations on the Digital Economy Framework Agreement, which covers AI, data governance, and cybersecurity, aiming for a $2 trillion digital economy by 2030.
Tracking the Economy through Firm Creation:Evidence from Real-Time Administrative Data
arXiv:2606.01307v1 Announce Type: new Abstract: We introduce a novel real-time dataset, Companies House Real-Time (CHRT), that captures daily firm creation and dissolution activity for the full population of UK-registered companies. CHRT provides a timely measure of business formation, becoming available months before official business demography statistics. We show that incorporation activity leads taxable business births and contains forward-looking information about employment and output growth. Consistent with this, a structural vector autoregression (SVAR) indicates that positive shocks to firm entry generate persistent increases in employment and output.
Strategic Preemption Under Shared Catastrophic Risk: The Suicide Region and the Race to Artificial General Intelligence
arXiv:2512.07526v3 Announce Type: replace-cross Abstract: We analyze a continuous-time preemption game with shared catastrophic externalities. When the cost of catastrophe is embedded in both players' payoffs, the risk term cancels out in the equilibrium indifference condition. This creates a "suicide region" where competitive pressures force rational agents to deploy despite negative risk-adjusted net present values. We apply this framework to the race for artificial general intelligence (AGI). We show that this suicide region widens as the cost of systemic ruin grows: higher catastrophic risk does not deter the race but instead enlarges the set of conditions under which rational actors deploy despite negative social value. We characterize the resulting welfare distortion against a social planner's benchmark and demonstrate how two complementary mechanisms - private liability and prize-sharing - can close the suicide region. Private liability raises the cost of unsafe deployment while prize-sharing reduces the strategic imperative to deploy first. "Warning shots" (sub-existential disasters) will fail to deter AGI acceleration, as the winner-takes-all nature of the race remains intact.
Will the IT consulting share price rout ever end?
Accenture made a fortune from previous tech revolutions but investors think AI could kill it, not make it stronger
AI Antitrust Issues Checklist - June 2026 | Vinson & Elkins LLP - JDSupra
As artificial intelligence (“AI”) transitions from a nascent technology into a central pillar of the global economy, public and private antitrust enforcement related to AI has likewise exploded.
AI’s Impact on SaaS Will Be Uneven. Here’s What Leaders Need to Know
Generative AI will affect SaaS platforms unevenly based on data types and output requirements, requiring leaders to carefully evaluate which tools to retain or replace.
Nvidia chases $200B CPU market with AI agent PCs
Nvidia is targeting the CPU market with new AI-powered PCs developed in partnership with Microsoft, Dell, and HP.
AWS reportedly to tuck Elon Musk's Grok into Bedrock, despite zero enterprise demand
The energy drink of frontier models.
Top 10 Economic & Litigation Consulting Firms (2026 Ranking)
Economic consulting is organized around four core areas: Competition economics, litigation support, disputes and investigations, and digital market analysis. The leading firms differentiate themselves through the strength of their economists, expert witnesses, and academic networks. Antitrust enforcement, AI ...
Optimal Transport-based Permutation-Invariant Bayesian Optimization of Offshore Wind Farm Layouts
arXiv:2606.00009v1 Announce Type: new Abstract: Bayesian Optimization (BO) is widely and successfully adopted for solving optimization problems having an expensive-to-evaluate, black-box, and non-convex objective function. However, the vanilla BO algorithm is not able to exploit possible symmetries characterizing the target problem. An intuitive case is given by optimal location problems, whose decision variables refer to a finite set of points within a continuous space, with the order of points not affecting the value of the objective function. We refer to this setting as optimization over layouts to distinguish from optimization over point-clouds where, instead, the order of points counts. As an instance of optimization over layouts we consider a real-life industrial-relevant application, that is the optimization of the layout of an offshore wind farm: given identical wind turbines, switching any pair of them has not any effect on the annual energy production. Based on Optimal Transport theory, we propose a Permutation-Invariant BO approach, namely PIBO, proved to provide better wind farm layouts when compared to the vanilla BO approach while cutting computation time roughly in half.
AI Is Already Rewiring the Aftermarket and Services
AI is driving improvements in aftermarket sales, parts identification, and customer support workflows, offering potential benefits for industrial companies.
NVIDIA, TSMC use AI to boost chip manufacturing | NVDA Stock News
TAIPEI, Taiwan, June 01, 2026 (GLOBE NEWSWIRE) -- NVIDIA GTC Taipei -- NVIDIA today announced that TSMC, the world’s leading semiconductor company, is using NVIDIA accelerated computing and AI to advance semiconductor design and manufacturing. As chips move to more advanced nodes, bringing ...
Grokers: Bottom-Up Inductive Comprehension and Write-Time Intelligence over Typed Knowledge Graphs
arXiv:2606.00050v1 Announce Type: new Abstract: We present Grokers, an architecture for building persistent, structured comprehension of typed knowledge graphs through bottom-up inductive traversal of dependency subgraphs. Unlike retrieval-augmented generation (RAG), which pays full comprehension cost at every query, Grokers pushes intelligence to write time: autonomous Groker agents analyze nodes in a typed stream graph, extract structured attributes via governed language model (LM) calls, and inductively compose that understanding upward through dependency relations, writing enriched typed attributes that serve all future queries at zero additional LM cost. We prove three formal properties: (1) the Byte-Identity Theorem, establishing that context blocks assembled from a transactionally-maintained denormalization index are byte-identical across LM turns between semantic changes, enabling KV-cache hit rates approaching 100%; (2) the Accumulation Monotonicity Theorem, establishing that the fraction of interactions resolved without LM calls is non-decreasing in the number of completed interactions under a governed wisdom library growth protocol; and (3) the Dual-Traversal Ordering Theorem, establishing that top-down generation and bottom-up comprehension are the unique correct traversal orderings for their respective tasks over a dependency DAG, and that their composition closes into a complete generation-comprehension cycle. We further present a deterministic alternative to embedding-based semantic search, with a synonym caching protocol whose LM fallback rate converges to zero for finite-vocabulary domains. A reference implementation is provided in the open-source Qbix / Safebox / Safebots stack.
AI Productivity Boost Is Overhyped | 3-Minute MLIV
Anna Edwards, Guy Johnson, Tom Mackenzie and Mark Cudmore break down today's key themes for analysts and investors on "Bloomberg: The Opening Trade." (Source: Bloomberg)
Total Factor Productivity and its determinants: an analysis of the relationship at firm level through unsupervised learning techniques
arXiv:2511.19627v2 Announce Type: replace Abstract: The paper is related to the identification of firm's features which serve as determinants for firm's total factor productivity through unsupervised learning techniques (principal component analysis, self organizing maps, clustering). This bottom-up approach can effectively manage the problem of the heterogeneity of the firms and provides new ways to look at firms' standard classifications. Using the large sample provided by the ORBIS database, the analyses covers the years before the outbreak of Covid-19 (2015-2019) and the immediate post-Covid period (year 2020). It has been shown that in both periods, the main determinants of productivity growth are related to profitability, credit/debts measures, cost and capital efficiency, and effort/efficiency of the R&D activity conducted by the firms. Finally, a linear relationship between determinants and productivity growth has been found.
SoftBank in Early Talks to Back $800 Million Agile Robots Round
German industrial robotics startup Agile Robots is in discussions to raise about $800 million in new funding from backers including SoftBank Group Corp., as investors seek out companies deploying artificial intelligence in real-world settings.
Munich’s Bayshore exits stealth with €6.9 million to automate legal and compliance workflows with AI
Bayshore, a Munich-based startup building an agentic AI platform that performs complex legal and compliance tasks in a reliable, explainable, and auditable way, has exited stealth mode with €6.9 million ($8 million) in Seed funding. The round was led by Earlybird Venture Capital, with participation from Lucid Capital, Booom, Heliad, and strategic angels. “Across industries, […]
Labor, Society & Culture
AI-driven labor displacement risks to remain low in near term, Bridgewater says | Reuters
Risks of widespread job losses from AI are expected to remain limited this year, according to Bridgewater Associates, with constraints on computing capacity and a resilient economy blunting the technology's near-term impact on employment.
MediaTek Pledges Hiring Spree to Boost Push Into New AI Spheres
Taiwanese chipmaker MediaTek Inc. will hike hiring to support a push into new AI activities, joining fellow tech firms in assuaging concerns about job losses in the era of artificial intelligence.
Guess Who’s Got an AI Edge in a Tough Job Market? - Bloomberg
Mentioning artificial intelligence to the graduating class of 2026 has been sure to get you booed. And why not? Fresh graduates have spent the past few years being told about the wonders of AI and watched seniors struggle to get a toehold in the labor market.
Pay Beliefs and the Amenity-Pay Tradeoff
arXiv:2606.02503v1 Announce Type: new Abstract: This paper studies how workers' beliefs about pay shape the tradeoffs between pay and workplace amenities. We design a multi-stage incentivized survey experiment that combines hypothetical choice experiments with elicited beliefs about starting salaries in real jobs and randomly varies the provision of explicit pay information. Although stated preferences imply sizable willingness to pay for amenities consistent with prior literature, baseline beliefs about salaries in real jobs are systematically biased along two margins: respondents under-predict starting salaries by 18% and expect higher-amenity jobs to pay more, substantially over-predicting the amenity-pay gradient. Exposure to pay information raises mean pay beliefs for similar jobs by 4% and reduces belief dispersion by 15%, but does not alter the strong positive association between perceived pay and advertised amenities, leaving the amenity-pay tradeoffs in stated choices essentially unchanged. While workers have strong preferences for workplace amenities, the tradeoffs they perceive deviate sharply from those present under full information.
Office Hours: How Do We Deal With the Inevitable Loss of Good Jobs to AI?
Four possible directions
Fresh Jobs Data May Help Fed Gauge How Much AI Strains US Labor Market - The Daily Upside
The US Bureau of Labor Statistics will be reporting May’s much-anticipated employment figures at the end of the week.
Week in review: How AI will derail careers | HR Dive
We’re rounding up last week’s stories, from the disconnect between front-line workers and leadership to the rise of learning as an HR priority.
Fake Plastic Voters: When Political Parties Can Use AI-Simulated Focus Groups
arXiv:2606.00043v1 Announce Type: new Abstract: Political parties strive to understand their electorates, and focus groups are a vital tool in these efforts. AI-enhanced simulation technologies (AESTs) enable synthetic focus groups in a fraction of the time (and cost), raising the question of when and how such simulated evidence can be used in campaign research. This paper develops a decision matrix to help party strategists match research needs to appropriate simulation technologies and to identify when to escalate to hybrid or fully human focus groups. The matrix combines three dimensions: strategic purpose, deployment risk, and empirical grounding of the simulation tool. Strategic purpose is the decisive dimension, as it determines what kind of evidence the focus group is meant to produce: observing how political meanings and identities emerge through interaction (Mode 1) or testing and refining campaign messages (Mode 2). The matrix shows that, given documented failure modes such as sycophancy, persona drift, and the suppression of minority viewpoints, AESTs cannot replace human interaction in Mode 1 at any risk level. Within Mode 2, suitability depends instead on deployment risk and on the empirical grounding. Yet even here, we caution that routine reliance on AESTs may erode the qualitative craft on which sound judgment depends.
Learning from Mistakes: Can LLM Self-Recover after Misalignment?
arXiv:2606.00003v1 Announce Type: new Abstract: Responsible AI initiatives place great emphasis on the safety of Large Language Model (LLM)-based systems. In particular, it has become standard practice to subject these models to an alignment procedure aimed at preventing harmful outputs. However, once aligned, a model is not guaranteed to maintain this alignment throughout its lifecycle. Moreover, the likelihood of misalignment increases as malicious actors may deliberately employ jailbreaking techniques to compromise LLM safety. To counter this, much research has focused on improving alignment methods and post-processing filters. In this paper, we introduce a new perspective on advancing LLM alignment: rather than developing stronger alignment techniques, we investigate the model's intrinsic ability to recover its alignment after corruption. We propose a methodology for modeling the safety trajectories of user-assistant interactions and for detecting recovery trends within them. We apply this approach to a jailbreaking scenario, presenting a preliminary recovery analysis based on a dataset of adversarial multi-turn dialogues and examining the influence of the content moderation model chosen for safety evaluation. Project page with an interactive data visualizer is available at https://lab-rococo-sapienza.github.io/LearningfromMistakes.
A phenomenon of AI-conformity: how algorithms change human moral decision-making
arXiv:2606.00013v1 Announce Type: new Abstract: Social conformity is a well-documented phenomenon in which individuals shift their opinions towards those of a social majority. As artificial intelligence (AI) becomes increasingly integrated into everyday life it may also create a novel source of influence giving rise to algorithmic conformity, mechanisms of which are poorly understood. The present study examined whether AI judgements affect moral decision-making in humans (n=165) adapting the classical Asch paradigm. Participants completed a series of moral dilemmas under three different conditions: in presence of social majority, with an AI model providing brief answers and with an AI model providing both answers and explanations of its choices. In all conditions the presented responses contradicted generally accepted moral norms. The results indicated that an AI model with a reasoning component affected the opinion of participants to a degree comparable to that of a human majority. These findings suggest that even moral judgements, despite their sensitivity and personal significance, may be susceptible to algorithmic conformity. However, the mechanism underlying algorithmic conformity appears to differ from the social one. Overall, the study challenges the assumption that moral decision-making lies in "AI inadmissibility zone" - a sphere that is considered as an area in which only human-made decisions are acceptable and highlights the need for a further investigation of this phenomenon as AI-based recommendations become increasingly embedded into human decision-making.
Beyond Categories of Caste: Examining Caste Bias and Morality in Text-to-Image AI Models
arXiv:2606.00039v1 Announce Type: new Abstract: Text-to-Image (T2I) models have shown promising utility across various domains. However, such models are also amplifying harmful societal biases in their outputs. In the context of South Asia, recent work has shown caste biases and stereotypes are being perpetuated through Generative AI (GenAI) systems. While this research offers extremely relevant insight into invisibilized narratives of caste discrimination through the GenAI system, they often treat caste as an identity category. Therefore, in this work we shift our ontology to focus on the relational aspect of caste. This enables us to develop a more nuanced understanding of the mechanics of caste discrimination by and through T2I models. Combining an algorithmic audit with critical discourse analysis, we draw on a conceptual frame challenging Brahminical Normativity to show how caste biases are perpetuated beyond the simple binaries of upper vs lower-caste categories. Our contributions are two-fold. Beyond challenging the categorical understanding of caste as a category, we propose an anti-caste approach to tackle the issue of caste bias and fairness in AI systems.
‘This is fine’ artist KC Green reaches agreement with AI startup Artisan
The artist behind the famous meme has settled a dispute with an AI company.
AI Firms Threaten Journalism's Future, Warns NYT Publisher at Global Congress
A.G. Sulzberger criticized AI firms for exploiting news content without authorization, calling for a unified media industry response to protect intellectual property.
Beyond Tool Adoption: A Practical Five-Stage Developmental Continuum for AI Literacy in Higher Education
arXiv:2606.00038v1 Announce Type: new Abstract: Artificial intelligence (AI) literacy is increasingly recognized as a foundational competency for all university graduates. Yet students' engagement with AI tools often clusters at two problematic extremes: avoidance driven by fear, mistrust, ethical concern, or lack of access, and uncritical reliance that produces fluent output while masking misunderstanding. Existing AI literacy frameworks provide valuable competency definitions, but most offer limited guidance for diagnosing where learners begin and how they progress toward responsible, critical engagement. This paper proposes a five-stage AI Literacy Continuum -- 1) Not Yet Engaged, 2) Uncritical Use, 3) Informed Use, 4) Critical Evaluation, and 5) Improvement -- that describes developmental orientations toward AI use in higher education. The continuum complements dimensional frameworks by providing educators with a practical diagnostic and instructional pathway aligned with international frameworks, including UNESCO and OECD. We present a design-based implementation case from North Carolina State University, where credit-bearing courses and intensive hands-on workshops engaged more than 330 participants between Fall 2024 and Spring 2026. Because the implementation did not use a validated pre/post instrument or comparison group, we frame the findings as observational and practice-based: participants exhibited behaviors consistent with movement from non-engagement or uncritical use toward informed engagement, while sustained and discipline-embedded experiences produced stronger evidence of critical evaluation and improvement-oriented practice. We discuss curricular pathways, equity considerations, assessment strategies, and argue that AI literacy should be understood not as tool adoption alone but as a developmental capacity to understand, evaluate, and responsibly apply AI systems in disciplinary and societal contexts.
Tracing GenAI Literacy: Uncovering Student-AI Interaction Patterns in Academic Writing through Epistemic Network Analysis
arXiv:2606.00040v1 Announce Type: new Abstract: As Generative AI (GenAI) becomes integral to education, fostering GenAI literacy is critical. However, current assessments largely rely on self-reported scales, lacking insights into how literacy manifests in actual learning processes. This study leverages Learning Analytics (LA) to bridge this gap. We collected interaction logs from 162 university students engaged in a GenAI-assisted abstract writing task. Using Epistemic Network Analysis (ENA), we modeled and compared the questioning strategies of students with varying GenAI literacy levels. Preliminary results reveal distinct interaction signatures: high-literacy students engage in iterative refinement and strategic questioning, while low-literacy students rely on direct generation commands. This work contributes to the workshop by demonstrating how process data can characterize GenAI literacy, paving the way for data-driven literacy assessment and real-time interventions.
Technology & Infrastructure
MindZero: Learning Online Mental Reasoning With Zero Annotations
arXiv:2606.00240v1 Announce Type: new Abstract: Effective real-world assistance requires AI agents with robust Theory of Mind (ToM): inferring human mental states from their behavior. Despite recent advances, several key challenges remain, including (1) online inference with robust uncertainty updates over multiple hypotheses; (2) efficient reasoning suitable for real-time assistance; and (3) the lack of ground-truth mental state annotations in real-world domains. We address these challenges by introducing MindZero, a self-supervised reinforcement learning framework that trains multimodal large language models (MLLMs) for efficient and robust online mental reasoning. During training, the model is rewarded for generating mental state hypotheses that maximize the likelihood of observed actions estimated by a planner, similar to model-based ToM reasoning. This method thus eliminates the need for explicit mental state annotations. After training, MindZero internalizes model-based reasoning into fast single-pass inference. We evaluate MindZero against baselines across challenging mental reasoning and AI assistance tasks in gridworld and household domains. We found that LLMs alone are insufficient; model-based methods improve accuracy but are slow, costly, and limited by backbone MLLM capacity. In contrast, MindZero enhances MLLMs' intrinsic ToM ability and significantly outperforms model-based methods in both accuracy and efficiency, showing that mental reasoning can be effectively learned as a self-supervised skill.
Agentic AI in the Enterprise: Key Trends and Use Cases for 2026 - AngelHack DevLabs
AI has moved past the chatbot phase. In 2026, it is planning, deciding, and acting on your behalf.
AgentOps: Operationalize agentic AI at scale with Amazon Bedrock AgentCore | Artificial Intelligence
Below, we’ve mapped out how agentic AI impacts each stage of your DevOps pipeline: Plan, Develop, Build, Test, Deploy & Release, Maintain and Monitor. The pillars apply irrespective of where you are in the lifecycle. From a responsible AI perspective, you need systematic risk management ...
AI Agent Culture: What Does it Mean For Your Lean Team?
Adopt AI agents to empower your lean team. Integrate digital assistants to handle repetitive work, boost productivity, and focus on growth.
The AI Landscape: June 2026 - by Jordamøn - AI Central
Microsoft Build opens on Monday with Satya Nadella’s keynote, centered on agentic AI orchestration, multi-agent frameworks, and the Azure AI Foundry platform. The session catalog is weighted toward agent debugging, production deployment, and cross-agent coordination.
Intel's Lip-Bu Tan on Agentic AI & Partner Networks
Intel's Lip-Bu Tan highlights why cross-sector partnerships are critical to sustaining a vibrant tech ecosystem. (Source: Bloomberg)
Deliberative Curation: A Protocol for Multi-Agent Knowledge Bases
arXiv:2606.00007v1 Announce Type: new Abstract: As AI agents transition from isolated tools to collaborative participants in shared knowledge ecosystems, governing collective knowledge curation becomes a critical challenge. Human platform governance mechanisms do not transfer directly: agent statelessness undermines deterrence-based sanctions, model homogeneity violates independence assumptions underlying crowd wisdom, and sycophancy collapses deliberative consensus. We propose a deliberative curation protocol combining three governance layers: (1) a knowledge artifact lifecycle formalized as a labeled transition system; (2) reputation-weighted deliberative voting integrating Beta Reputation with EigenTrust amplification; and (3) graduated sanctions adapted for stateless agents, including broken agent handling distinguishing malfunction from adversarial behavior. We evaluate the protocol through agent-based simulation with 100 agents across seven behavioral archetypes under two adversity scenarios (30 seeds, paired t-tests). The protocol trades modest precision under benign conditions for substantially better resilience under adversity: 0.826 vs 0.791 for majority vote under moderate adversity (p<0.001), widening to 0.807 vs 0.740 under stress (p<0.001). The protocol degrades roughly three times more slowly than majority vote. Ablation analysis identifies commit-reveal vote concealment as the most impactful single component (8.2-8.6pp precision improvement, p<0.001), outperforming reputation weighting and deliberation combined. Graduated sanctions were not exercised in simulation and remain empirically unvalidated.
Why HPE Is Expanding Its Focus On Agentic AI Infrastructure
HPE expands its enterprise computing portfolio with infrastructure designed for agentic artificial intelligence, advanced data processing, and next-generation workloads.
Why the agentic AI-powered ROC is the new frontline of defense | Federal News Network
If one agent finds a threat, it ... mission impact. The threat intelligence feed: Agent-based AI relies on strong connections to threat databases. These agents make decisions a human would normally make — such as updating firewalls or applying IPS signatures — based on specific system needs, whether ...
Agents on a Tree: Pathwise Coordination for Multi-Objective Molecular Optimization
arXiv:2606.00008v1 Announce Type: new Abstract: Multi-objective molecular optimization requires searching vast chemical spaces under conflicting objectives, where early design decisions strongly constrain downstream outcomes. Existing methods typically rely on a single policy or fixed scalarization, which limits their ability to represent diverse trade-offs and to explore multiple promising design trajectories. We propose ATOM, a multi-agent framework that formulates molecular optimization as a tree-structured search. Each node corresponds to an atomic operation and hosts an agent specialized for a particular objective or decision context. Agents coordinate along different paths of the tree rather than enforcing a global consensus, enabling the method to maintain and compare alternative molecular evolution trajectories. A global memory of past optimization behaviors further supports balanced exploration and exploitation across objectives. This tree-structured interaction enables reasoning over long-horizon dependencies inherent in molecular design. Experiments on challenging multi-objective benchmarks involving activity, synthesizability, and ADMET-related properties show that ATOM consistently achieves improved Pareto coverage and hypervolume over strong baselines. These results demonstrate the effectiveness of pathwise multi-agent coordination for molecular optimization. Code is available at https://anonymous.4open.science/r/ATOM-41CE.
Council Post: The New Enterprise Stack: Why Process And Context Must Converge For Agentic AI
The next chapter of enterprise AI must move beyond simple workflows and embrace a dual architecture: a system of process and a system of context.
China’s Lab-Grown Diamonds Emerge as Unlikely Winner in AI Boom
China’s lab-grown diamonds are emerging as a surprising beneficiary of the artificial intelligence boom, with demand climbing while they gain traction as a key component in advanced chipmaking.
NVIDIA and Microsoft Reinvent Windows PCs for the Age of Personal AI | NVIDIA Newsroom
NVIDIA today unveiled NVIDIA RTX Spark™, a new superchip that reinvents Windows PCs for the era of personal AI agents — offering a new class of computer that moves from tool to teammate.
What are AI PCs that Nvidia's Jensen Huang is betting on? | Reuters
They do not have to rely on cloud data centers powering most AI applications like Open AI 's ChatGPT and Anthropic's Claude, and some variants can also support training AI models — a compute-intensive task typically done on servers — locally on the device.
ASML spin-out Invisix raises €20m to support chip x-ray technology
Semiconductor metrology startup Invisix has raised €20 million ($23m) in a seed funding round to support its soft x-ray technology. The company, which is based in Eindhoven, in the Netherlands, has been developing the technology inside ASML since 2015. In 2025 it was spun out into Invisix by Christina Porter and Sietse van der Post, […]
Intel bets big on AI infrastructure with new Xeon chips - The Economic Times
Intel has unveiled new data center products. This includes the next-generation Xeon 6+ processors and advanced AI accelerators. These innovations are designed to support the rapidly expanding needs of artificial intelligence infrastructure. The company is betting on increased demand for these ...
HPE Pulls Forward Long-Term Targets as Surging AI Compute Demand Boosts Revenue
The move comes after reporting second-quarter earnings that blew past Wall Street expectations, citing rampant compute demand from customers transitioning to artificial-intelligence tools.
From innovation hub to infrastructure powerhouse: What venture backed AI startups need next - The Business Journals — Venture Capital | AlphaMaven
From innovation hub to infrastructure powerhouse: What venture backed AI startups need next The Business Journals | Venture Capital on AlphaMaven. | AlphaMaven
Ohio hits pause on datacenter tax breaks draining its coffers
Buckeye State found it had inadvertently joined the billion dollar losers' club
Jurisdictional lines at heart of US data center hookup talks
US state utility regulators are awaiting federal guidance that could standardize how energy-intensive artificial intelligence data centers hook up to the electric grid.
SK Hynix Plans to Double Capacity to Ease Memory Chip Crunch
SK Hynix Inc. plans to double its memory chip capacity over the coming half-decade, a major expansion that should help ease a global shortage of an essential component of AI.
Intel and pals cram 36,864 CPU cores into a 100kW rack while chasing the agentic AI dragon
Meanwhile, Intel and SambaNova's disaggregated inference blueprint lands its first customer
Agent-led devs need serverless OpenSearch, Amazon claims
System relies on a proprietary storage layer as AWS moves to separate storage and compute to fit mega AI demands
The Case For Pragmatism in the AI Infrastructure Boom
As AI investment accelerates, data center operators can draw on lessons from previous cycles to add capacity while managing power, volatility and risk.
Intel Diamond Rapids to boost core counts to 192, but RIP Hyperthreading
Intel's upcoming Diamond Rapids processors are set to feature 192 cores, while the company moves away from Hyperthreading technology.
Make Mechanistic Interpretability Auditable: A Call to Develop Guidelines via Continuous Collaborative Reviewing
arXiv:2606.00033v1 Announce Type: new Abstract: While mechanistic interpretability (MI) has produced important insights into neural network internals, the field has yet to establish a standardized system to audit experiments. As such, many of its findings remain underutilized in safety-critical applications such as medical AI and autonomous systems, as stakeholders cannot certify their validity. Recent work demonstrates this concretely: two papers found conflicting conclusions for the same behavior, and a third study revealed that both were partially correct but incomparable due to methodological inconsistencies. Without standardized auditing, such ambiguities hinder adoption in high-stakes contexts requiring strong correctness guarantees. We call for the MI community to work towards developing a novel reviewing system that complements peer review via: (1) Continuous reviewing supported by a \emph{Collaborative Reviewing Platform} where meta-science results and discussions (such as critiques, negative results, post-hoc extensions, reproductions, replications, and partial results) that fit outside of papers are organized and discussed, allowing for comments and revisions to be made at any time (2) Generalizing good practices found on this platform into expert-verified guidelines and protocols to improve auditing efficiency, and (3) Source-based auditing systems that track arguments which claims depend on. This position paper encourages constructive debate over the necessity, design and implementation of such a framework, providing early concrete examples to help catalyze these dialogues. Overall, we propose that auditing MI itself is essential for its application in AI safety, industry, and governance.
CAST: Non-Privileged Clipped Asymmetric Self-Teaching with Advantage Flipping for GRPO
arXiv:2606.00172v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR), especially Group Relative Policy Optimization (GRPO), has been widely used to improve reasoning in large language models. However, outcome-level rewards provide only sparse supervision, and group-relative advantages vanish when all sampled trajectories for a prompt are either correct or incorrect. On-Policy Self-Distillation (OPSD) offers dense token-level guidance, but its token preferences are not necessarily aligned with trajectory correctness; empirical diagnostics show that OPSD signals behave differently on correct and incorrect rollouts, with teacher-positive and teacher-negative gap signals exhibiting different noise profiles. These diagnostics are conducted under an OPSD-style privileged teacher context for analysis only, whereas CAST training uses answer-free self-teacher scoring.Motivated by these observations, this work proposes CAST, an answer-free self-distillation method for GRPO-style RLVR. CAST keeps the verifier-grounded GRPO objective, but uses a stop-gradient self-teacher to shape token-level advantages according to trajectory correctness. Unlike prior self-distilled RLVR methods, CAST does not require reference-solution-conditioned teacher scoring, keeps the self-teacher log-probability gap active throughout training, and applies bidirectional local advantage sign reversal: teacher-negative tokens in correct trajectories can receive negative token-level advantages, while teacher-positive tokens in incorrect trajectories can receive bounded positive local advantages. For zero-variance all-correct and all-wrong groups, CAST assigns bounded sign-constrained base advantages, so these otherwise zero-gradient groups can contribute verifier-signed token feedback. Experiments on mathematical reasoning show that CAST improves RLVR training while retaining a lightweight, verifier-grounded trajectory-level objective.
Product-Aware Deep Autoencoders for Robust Process Monitoring in Multi-Product Cyber-Physical Systems
arXiv:2606.00052v1 Announce Type: new Abstract: As Industry 4.0 accelerates the integration of Cyber-Physical Systems (CPS) in manufacturing, robust anomaly detection has become critical for ensuring process safety and security. Current data-driven approaches typically employ "product-agnostic" or global models trained on the aggregate of all normal operating data. However, modern industrial facilities frequently operate under diverse product grades. While computationally simple, these global models inherently expand their decision boundaries to accommodate the variance of multiple modes, creating a "blind spot" where subtle anomalies or targeted cyber-physical attacks may be masked by the wide acceptance region of the model. In this work, we first demonstrate that the vulnerability described above is present in global-agnostic models operating across multiple product grades. We then present a Product-Aware Autoencoder as a principled mitigation that restricts the learning domain to grade-specific distributions. While this approach reduces the identified blind-spot risk, we do not claim it as the optimal mitigation among all possible alternatives. We rigorously validate this approach against a Global Agnostic baseline using the Extended Tennessee Eastman Process (TEP) benchmark. Our empirical results indicate that the Product-Aware framework performs comparably to the global baseline on standard detection metrics, while offering improved robustness to product-grade-specific operating modes. Most critically, stress tests simulating our hypothetical attack scenarios reveal that while the global model fails to detect operational deviations in 77.8% of the scenarios, the product-aware system achieves 100% detection accuracy. These findings suggest that, in flexible manufacturing environments, generalized anomaly detectors can pose non-trivial security risks, motivating a shift toward mode-aware diagnostic architectures.
Hackers trick Meta AI support bot to infiltrate Obama White House Instagram account
Breach confirmed by Meta raises concerns about how safe it is to rely on AI for key security measures such as passwords Hackers used Meta’s AI-powered support chatbot to infiltrate high-profile Instagram accounts, the company confirmed on Monday, saying it had resolved the problem after researchers exposed it. The targets ranged from Barack Obama’s White House account to Sephora and the US Space Force chief master sergeant, according to reporting from 404 Media. Everyday users complained of similar hijackings on Reddit and X over the weekend. Continue reading...
Anthropic's browser agent got hijacked 31.5% of the time
Research shows that Anthropic's browser agent was vulnerable to hijacking in nearly a third of attempts before security safeguards were implemented.
AI Integrity: Defending Against Backdoors and Secret Loyalties
arXiv:2606.00036v1 Announce Type: new Abstract: AI integrity means ensuring AI systems are free from secret or unauthorized modifications that could compromise their behavior. Integrity represents one pillar of the confidentiality, integrity, and availability (CIA) triad in information security: confidentiality preserves secrecy of sensitive information, integrity ensures data remain authentic and uncorrupted, and availability keeps systems operational when needed. While confidentiality receives some attention through efforts like RAND's Securing AI Model Weights report, and availability is naturally prioritized by market forces, AI integrity receives insufficient attention despite its importance to national security.
Claude Mythos exposed a hard truth: Your enterprise patching process is way too slow
The Claude Mythos vulnerability highlights critical delays in enterprise security patching.
TIGER: Traceable Inference with Graph-Based Evidence Routing for Mitigating Hallucinations in Multimodal Generation
arXiv:2606.00232v1 Announce Type: new Abstract: We study fact-level repair for multimodal generation, where a fluent output may contain specific facts that are not supported by the input. Existing inference-time repair methods often generate feedback by jointly conditioning on the input and the current output. This design has two limitations: hallucinated claims in the output can bias the model's interpretation of the input, and free-form feedback cannot be ranked or scheduled at the fact level. We present TIGER, an inference-time framework that redesigns feedback for localized repair. TIGER independently extracts an observation graph from the input and a claim graph from the current output, then assigns each claim a graph-conditioned risk score based on support and conflict. The model repairs selected high-risk claims while keeping the backbone frozen. We provide a convergence analysis showing that the expected total risk decreases geometrically to an explicit asymptotic bound under mild assumptions. Experiments across four cross-modal paths, including image-to-text, image+text-to-text, audio-to-text, and video-to-text, show that TIGER reduces unsupported content while preserving task quality. The gains hold across multiple backbones, and a CrisisFACTS case study suggests that the same repair mechanism can improve grounding in multi-source settings.
Shai-Hulud malware worms Red Hat npm package versions downloaded 80K times a week
TeamPCP? Or copycat malware dev?
AI was supposed to prevent downtime. Instead, it’s creating new kinds of outages - Fast Company
New research suggests AI itself is increasingly becoming the source of costly operational failures.
Position Paper: Post-Solve Robustness in Decision Engines: Feasible Regions and Smoothness Under Perturbations
arXiv:2606.00002v1 Announce Type: new Abstract: Mixed-Integer Linear Programming (MILP) decision engines routinely output nominally optimal plans for high-stakes industrial systems. Yet deployment rarely matches solve-time assumptions: small perturbations in costs, demands, or resource availability can invalidate feasibility or trigger discontinuous shifts to qualitatively different solutions. We argue that this post-solve robustness gap is a missing layer in today's optimization pipelines and a missing evaluation dimension for learning-enabled decision systems. Rather than replacing robust optimization or stochastic programming, the proposed layer audits a solved incumbent and returns solver-backed evidence about how far that solution can be trusted. We formalize two central objects: (i) an $\epsilon$-near-optimal feasible neighborhood in parameter space, capturing when an incumbent remains feasible and near-optimal under perturbations, and (ii) solution smoothness in decision space, capturing whether nearby alternatives with small combinatorial edits remain competitive. We then synthesize the most relevant partial answers from sensitivity and stability analysis, robust optimization, neighborhood search, adversarial testing, and learning-based enhancements, and articulate an agenda for a unified post-solve robustness layer. Concretely, we call for certified inner approximations around the incumbent, probabilistic robustness estimation with calibrated uncertainty, adversarial robustness margins, and learning-based prediction and explanation aligned with solver-backed verification. We conclude with a compact reporting template and evaluation protocol that would make robustness a first-class output of decision engines.
AI helped researchers bypass Apple M5 defenses
In simpler terms, MIE helps the chip and operating system check whether software touches memory in suspicious ways. That makes many older attack tricks harder to pull off. That is why Calif's claim warrants attention. The researchers say they found a way around those protections with help from Mythos Preview. That suggests AI could speed up the hunt for flaws, even in systems with advanced built-in defenses. AI CYBERSECURITY RISKS ...
Guide issued for healthcare organizations on cyber governance frameworks for secure AI implementation | AHA News
The Health Sector Coordinating Council’s Cybersecurity Working Group has released a guide to help healthcare organizations establish cyber governance frameworks for secure artificial intelligence implementation. The guide addresses challenges in identifying and mitigating AI-specific cyber ...
The 2026 Cybersecurity Inflection Point: AI, Identity, and the Collapse of Traditional Defense Assumptions - My SEO Directory
In an era where defense windows can shrink to mere minutes, speed and intelligence are critical competitive advantages for cybersecurity teams. Forward-thinking organizations are already adopting predictive security models that leverage AI to anticipate attack patterns, prioritize risks, and ...
Adoption, Deployment & Impact
The automation illusion: Why AI is making COOs’ jobs harder, not easier
The executives responsible for keeping the world's biggest companies running thought AI would simplify their jobs. They were wrong.
How small businesses can leverage AI
This article is from Making AI Work, MIT Technology Review’s limited-run newsletter examining how to apply LLMs across industries. To receive it in your inbox,sign up here. From accounting to design to market research and product development, there’s a staggering breadth of skills needed to run a business. A large company can hire experts to…
How 'confused' AI rollout hurts firms and baffles staff
Some firms are putting pressure on staff to use AI, but have not thought through their AI rollout.
What Startups Get Wrong About AI Automation
A clear look at common mistakes startups make with AI automation, including poor planning, scaling too fast, and integration issues, plus how to build simpler systems that work.
Community Health Workers Are Right to Distrust AI Solutions - ICTworks
Four RCTs in LMICs across five ... for sector-wide deployment. The $60 million EVAH evaluation initiative is a start. Make LMIC-validated evidence a prerequisite for funding at scale, not a nice-to-have. Email a link to a friend (Opens in new window) Email ... Filed Under: Healthcare More About: ...
5 Tips for Giving Your AI Agents Better Business Context
But as enterprises shift from AI ... systems, authorizing them to operate across multiple spheres, they’re discovering that generic LLMs and powerful models can’t deliver business accuracy. A recent Gartner poll found that · 75% of IT leaders implemented AI agents, but only ...
Northern Ireland Ahead Of The Game On AI Adoption - Microsoft Report - Business Eye
Northern Ireland Ahead Of The Game On AI Adoption - Microsoft Report
Advantech's Tsai on Nvidia Collaboration, AI Strategy
Advantech COO and President of the Intelligent Systems Sector Linda Tsai discusses the company's collaboration with Nvidia to power next-generation AI solutions. She also lays out the company's strategic vision behind integrating AI across products and services. Linda speaks with Stephen Engle from the sidelines of 'Computex 2026' in Taipei. (Source: Bloomberg)
Metaverse in Smart Cities: Transforming Urban Life and Governance
arXiv:2606.00032v1 Announce Type: new Abstract: The integration of metaverse technologies within Smart Cities is transforming urban governance and citizen engagement. Despite the increasing academic and industry interest, research on the practical applications of the metaverse in SCs remains fragmented. This study addresses this gap through a systematic literature review on how metaverse-driven solutions impact economic transformation, governance, mobility, sustainability, and social interactions in urban environments. The study synthesizes findings from existing applications and case studies, such as Metaverse Seoul, Dubai's Metaverse Strategy, Virtual Helsinki, and Tampere's CitiVerse initiative, to illustrate the diverse ways in which cities are leveraging metaverse technologies. These applications demonstrate the metaverse's potential in digital governance, Artificial Intelligence (AI)-driven urban planning, e-participation, transportation optimization, and climate resilience strategies. This research contributes to the field by providing a comprehensive framework for understanding the benefits and challenges of metaverse-driven SC models. The findings suggest that while metaverse adoption in SCs presents significant advantages in efficiency, participation, and innovation, it also entails challenges related to technological accessibility, governance frameworks, and security measures that must be addressed for broad uptake. The study's impact extends to policymakers, urban planners, and technology developers by offering strategic insights for responsible and inclusive metaverse adoption. Ultimately, this study provides a structured roadmap for integrating metaverse technologies into smart urban ecosystems, ensuring their long-term viability, accessibility, and effectiveness in shaping the cities of the future.
China’s BrainCo Sees Bionic Hand Sales Boom From Robot Makers
Chinese neurotechnology startup and prosthetics developer BrainCo expects sales of its robotic hands to surge this year as demand grows from the country’s fast-expanding humanoid robotics industry.
Interactive Brokers Unveils AI-Driven Trading with Claude for Enhanced Portfolio Insights
Interactive Brokers introduced agentic trading with Claude to assist with stock research and trade idea generation, requiring client approval for security.
VinFast, Autobrains, NVIDIA Launch Level 4 Autonomous Driving for Southeast Asia's Robotaxi Revolution
The companies are developing a Level 4 autonomous driving system for Southeast Asia to reduce costs and enhance performance in challenging traffic conditions.
Five Ways AI Is Transforming Cancer Care—and Companies That Are Making It Happen | AJMC
AI is poised to transform oncology with innovative tools enhancing diagnosis, treatment, and clinical trials, despite some wariness from clinicians and patients.
BenQ unveils AI ecosystem spanning industry & healthcare
The exhibition was organised around ... and AI Healthcare & Wellness. The layout was intended to show how artificial intelligence is moving beyond the pilot stage and into commercial and industrial use. Several BenQ-affiliated companies took part, including Qisda, AEWIN Technologies, Arivor Technologies, Alpha Networks, DFI, MetaAge, Grandsys, D8ai, Partner Tech, WiXtar, APLEX Technology, DATA IMAGE and URSROBOT. Together, they presented hardware, software, systems integration and sector-specific ...
Generative AI and Sales Productivity: Field Experiments in Online Retail
arXiv:2510.12049v4 Announce Type: replace Abstract: We quantify the short-term impact of Generative Artificial Intelligence (GenAI) on sales performance through a series of large-scale randomized field experiments involving millions of users and products at a leading cross-border online retail platform. Over 2023-2024, the platform integrated GenAI into seven consumer-facing business workflows spanning customer service, consumer-product matching, advertising, and seller services. We find that GenAI adoption increases sales in most workflows, with effects ranging from no detectable impact to $16.3\%$, depending on GenAI's marginal contribution relative to baseline firm practices. Across the four GenAI applications with positive sales effects, the implied annual incremental value is roughly $\$5-$an economically meaningful impact given the retailer's scale and the early stage of GenAI adoption. The gains operate primarily through higher conversion rates rather than larger cart values, consistent with GenAI improving the shopping experience by reducing search, information, communication, and personalization frictions. Importantly, these effects are not associated with worse post-purchase outcomes, as product return rates and customer ratings do not deteriorate. Finally, we document substantial demand-side heterogeneity, with larger gains for less experienced consumers. Our findings provide novel, large-scale causal evidence on how GenAI shapes sales productivity in online retail, highlighting both its immediate value and broader potential.
TransResAI: A Compound AI System for Coastal Transportation Resilience
arXiv:2606.00042v1 Announce Type: new Abstract: Coastal flooding increasingly threatens transportation infrastructure, yet the analytical tools needed for resilience management remain difficult for many non-specialist practitioners to use. This study presents TransResAI, a compound AI system that supports analysis of flood-aware transportation resilience via natural-language interactions. The system integrates a locally deployable Large Language Model (LLM) with modules for task decomposition, secure code generation, geospatial analysis, retrieval-augmented generation, and interactive map rendering. TransResAI links MATSim flood-scenario simulation outputs, OpenStreetMap-derived flood-risk networks, equity-focused demographic indicators, and regional documents in Hampton Roads, Virginia. A structured user study with domain experts demonstrated that TransResAI reduced task completion time by 80-88% relative to conventional GIS workflows, compressing analytical tasks from a mean of 197.1 seconds to 29.7 seconds and visualization tasks from 364.0 seconds to 46.1 seconds, while maintaining mean accuracy of 4.60/5.00 and task completion rates exceeding 94%. These findings demonstrate that compound AI architectures bridge the gap between general-purpose language models and specialized domain knowledge, as well as the quantitative rigor required for infrastructure resilience, providing transportation agencies and communities with faster, more accessible analytical tools for decision-making under growing climate uncertainty.
Overcoming Skepticism and Driving AI Adoption in Nursing - Emerj Artificial Intelligence Research
Nursing documentation has become an operational bottleneck that AI cannot fix without deep workflow alignment and disciplined change‑management. Nurses now spend up to 41% of their time on EHRs, according to the U.S. Department of Health and Human Services, and validated stress‑monitoring ...
AI Savings Misses 'Should Be Making Executives Uncomfortable,' Bain Says
Cost savings from automation are broadly falling short of projections, according to a new Bain & Co. global survey of large companies. The missed
Netflix wiz creates app to slash AI bills, then open sources it
Project Headroom could save you big money, too.
Rerankers Aren’t Magic Either: When the Cross-Encoder Layer Is Worth the Cost
An evaluation of when to use cross-encoder layers in enterprise document intelligence.
Geopolitics, Policy & Governance
China Aims A.I. at Predicting Who Could Pose a Political Risk
New research examines how a Chinese company struggled to develop its predictive surveillance technology while U.S. restrictions were in place.
GCHQ outlines AI-driven cyber defence programme for protecting critical infrastructure | Digital Watch Observatory
Agency director confirms plans to deploy agentic AI for protecting critical national infrastructure, with quantum computing and space security also flagged as priorities.
China tightens grip on tech giants as Xi demands industrial push
China is pressuring platform companies to shift from consumer-focused innovation to supporting national industrial goals. President Xi Jinping has designated six priority sectors as key to industrial strategy through 2030.
US clarifies non-export of AI chip flows to Chinese firms outside of China
The US Bureau of Industry and Security clarified that exporting advanced AI semiconductor chips to Chinese firms requires a license, regardless of whether the parent company is based outside of China.
Beyond the three-model map: where the UK's AI strategy actually sits - Resultsense
The US-EU-China map most UK leaders use to anticipate AI regulation is a poor predictor. A text analysis of 56 national strategies places the UK in a growth-and-delivery cluster — alongside America, but also Brazil and Bangladesh.
Draft federal AI strategy aims to scale up adoption, offer literacy training by 2031 | CBC News
A draft version of Canada's national AI strategy outlines a drive to scale up business adoption and provide all Canadians access to free AI literacy training, but is short on specifics regarding how the federal government will protect Canadians from the technology's potentially harmful effects.
Understanding the Role of Algorithm Registers in AI Governance Through Comparative Analysis of China and the UK
arXiv:2606.00035v1 Announce Type: new Abstract: Algorithm registers are increasingly being both considered and deployed as instruments in AI governance. They are often expected to deliver transparency; however, in practice their design, scope, and implementation vary substantially. Currently, we lack a holistic understanding of the potential roles that registers might play in AI governance, and how different design choices both shape and reflect those roles. This paper therefore asks how do algorithm registers differ across jurisdictions, and what do these differences reveal about their roles in AI governance? Towards this, we conduct a comparative analysis of two influential but contrasting algorithm registration mechanisms, China's Beian system and the UK's Algorithmic Transparency Recording Standard (ATRS), drawing on publicly available regulatory documents, registration guidelines, and registry data. Crucially, our analysis shows that an algorithm register, depending on its design and implementation, can serve functions beyond transparency, including pre-market approval, enabling ecosystem-level understanding, and acting as a broader regulatory infrastructure. As algorithm registries proliferate globally, we stress the importance of researchers and policymakers considering and examining the concrete governance functions that algorithm registries can perform as a result of their design and institutional context, rather than approaching them primarily through a transparency lens.
Update Opacity: Epistemic Accessibility and Governance Under AI System Change
arXiv:2606.00037v1 Announce Type: new Abstract: Machine learning models embedded in deployed AI systems are routinely updated to maintain correct functioning over time. Yet such updates can generate update opacity: users may not be able to understand why the same input now yields a different output. We argue that update opacity is best understood as a diachronic failure of epistemic accessibility: the problem is that materially relevant changes may fail to remain accessible to human users in forms that support understanding, calibrated reliance, and appropriate action under real role- and time-specific constraints. This makes update opacity a governance problem. Not all change is equally relevant, and disclosing every update would itself undermine use through overload. To address this problem, we combine two complementary governance approaches: the EU AI Act, which helps specify the system-level perimeter of normatively relevant change, and Machine Learning Operations, which provides operational tools for tracking and comparing change over time. On this basis, we propose a framework that models system change through trustworthiness profiles and trustworthiness levels, and uses threshold-based disclosure to surface materially relevant within-envelope change to different stakeholders over time. We illustrate the approach with a medical AI example and derive practical implications for lifecycle documentation, post-market monitoring, and update disclosure.
From Parliamentary Rhetoric to Enacted Law: An NLP Pipeline for Semantic Auditing of the Greek Legislative Process
arXiv:2606.00030v1 Announce Type: new Abstract: The Greek legislative framework is characterized by intricate cross-referencing, frequent amendments, and limited machine-readable access, hindering transparency and civic engagement. Traditional bulk-archiving approaches are computationally expensive and fail to capture political relevance. We present a multimodal computational pipeline that bridges parliamentary discourse with enacted legislation. Applying Natural Language Processing (NLP) to 2025 Hellenic Parliament transcripts, we extracted 534 unique law citations and used debate frequency as an empirical signal to identify politically salient laws. A headless browser architecture enables automated acquisition of official Government Gazette documents despite anti-scraping barriers. Using Large Language Models (LLMs), we conduct a semantic audit of legislative quality. Our analysis reveals an "Illusion of Simplicity", where laws framed as simplifications exhibit high structural complexity and ambiguity. A typology of 312 ambiguity instances shows that 45 percent stem from vague terminology and 25 percent from deferred executive delegation. We introduce the Political Discrepancy Index (PDI), evaluating alignment between ministerial promises and enacted law. Across three high-frequency laws (4808/2021, 4412/2016, 4662/2020), the dominant outcome is Deferral, with commitments shifted to future Ministerial Decisions. Cross-reference network analysis confirms a highly entangled legal system, with foundational provisions among the most frequently amended. The pipeline produces a semantically linked dataset and an interactive auditing interface for scalable analysis of legislative processes.
Driving global health equity with artificial intelligence: the global initiative on AI for health (GI-AI4H) | npj Health Systems
The Global Initiative on Artificial Intelligence for Health (GI-AI4H) is a World Health Organization-led collaboration with the International Telecommunication Union and the World Intellectual Property Organization to support safe, ethical, and equitable Artificial Intelligence (AI) adoption ...
NYDFS issues dual advisories on ‘frontier AI’ cybersecurity risks and heightened threat preparedness | Orrick, Herrington & Sutcliffe LLP - JDSupra
On May 21, NYDFS issued two industry letters addressing cybersecurity risks in a “heightened threat environment.” The first advisory warns regulated entities about heightened cybersecurity risks...
How AI Governance Is Being Built In Real Time, And What Comes Next - New Technology - United States
Federal AI governance is evolving through enforcement actions, procurement standards, and state legislation while Congress debates comprehensive frameworks.
May 2026 US Tech Policy Roundup | TechPolicy.Press
A roundup of the most important US tech policy developments in the federal government, the courts, and beyond from Freedman Consulting and Tech Policy Press.
Acemoglu: Pope Leo could’ve gone further in his encyclical on AI—this technology demands deep thought | Mint
The pope raised the question of what AI's purpose ought to be. This is a good starting point for questions like what choices humanity must make in response to the technology. Here are some policy options.
145 AI laws passed in 2025 and privacy teams aren't catching a break - Help Net Security
AI adoption brings new privacy risks as shadow AI, compliance requirements, and consumer requests put pressure on organizations.
Dutch gov't urged to critically examine whether AI can really solve healthcare problems | NL Times
It urged the Dutch government and parliament to critically examine whether AI truly offers real answers to the challenges in the sector, and whether using the technology is desirable. The CEG examined potential AI solutions for the widely recognized challenges in healthcare - staff shortages, ...
Researchers Create First-of-Its-Kind Index of Evolving Policy Landscape Around Health Care AI | Mount Sinai - New York
The researchers analyzed 240 health ... efforts are accelerating worldwide, though no single, unified framework currently exists to guide how AI should be deployed, monitored, and governed in clinical settings....
EU appoints expert panels for AI Act enforcement
The European Commission has appointed a 60-member Scientific Panel and a 174-member Advisory Forum to support the enforcement of the EU’s AI Act.
Uber, Autobrains, and Nvidia Launch Robotaxi Pilot in Munich Amid Regulatory Challenges
Uber partners with Autobrains and Nvidia to launch a robotaxi service in Munich, aiming for scalable European expansion pending regulatory approvals.
You can't do AI ethics without AI ethicists
A European standard could be set out to change that: the Competence requirements for professional AI ethicists (EN 18274). It just received the FV (final vote) result: 100% approval with no comments. EN 18274, developed under CEN-CENELEC JTC 21 passed its final vote and is expected to be published ...
UK privacy regulator to publish new AI strategy this year after government request
The UK privacy regulator is set to publish a new AI strategy later this year following a formal request from the government.
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