Fri 26 June 2026
Daily Brief — Curated and contextualised by Best Practice AI
Governments Plan Retraining, Samsung Pours Billions, and Apple Hikes Prices
TL;DRA coalition of employers and state governments is launching a national strategy to manage AI-driven labor displacement. Meanwhile, Samsung and SK Hynix are preparing massive capital expenditures to address memory shortages. Apple has responded to these supply chain pressures by raising MacBook and iPad prices by 20%, contributing to a $263 billion loss in market capitalization. Separately, the Trump administration has requested that OpenAI stagger its model releases to mitigate potential security risks.
The stories that matter most
Selected and contextualised by the Best Practice AI team
The New Push to Ready Millions for AI Career Upheaval
A coalition of employers and state governments says it is developing a sweeping strategy to help workers respond to the AI age.
ON Semiconductor to Buy Synaptics in All-Stock Deal With $7 Billion Enterprise Value
ON Semiconductor said Synaptics’s AI compute platform, human-machine interface technology, and connectivity solutions would help it meet demand for increasingly capable AI solutions that can interact with the physical world.
SemiAnalysis Xie on Asia AI Supply Chain
Myron Xie, AI supply chain research lead at SemiAnalysis, explains who, within Asia's hardware suppliers, are standing out as the winners in the market's surge in AI demand. He speaks with Shery Ahn on Bloomberg Tech: Asia. (Source: Bloomberg)
Samsung, SK Hynix Reportedly Preparing Huge AI Spending Push
Samsung Electronics Co. and SK Hynix Inc. are preparing to announce hundreds of billions of dollars worth in new investments on Monday, according to South Korean media reports this week.
Trump Administration Asks OpenAI to Stagger AI Model Release
The Trump administration has asked OpenAI to stagger the release of an upcoming powerful artificial intelligence model, according to a person familiar with the matter, nearly two weeks after rival Anthropic PBC suspended its most capable offerings from the market under regulatory pressure.
The Shift to Agentic AI: Evidence from Codex
arXiv:2606.26959v1 Announce Type: new Abstract: We analyze usage data from OpenAI's Codex tool to present large-scale evidence of how agentic AI technology, which can take actions on a user's behalf, changes how people work. We use an automated, privacy-protecting pipeline to contrast usage across three populations: external personal-account users, external organizational-account users, and workers within OpenAI. We find that agentic AI usage is growing rapidly: the number of active users has grown more than fivefold in the first half of 2026, with the most rapid increase occurring outside the initial audience of software developers. Uptake is uneven: within OpenAI, Codex usage is nearly universal and has largely replaced business usage of ChatGPT. We document a similar shift to agentic tooling outside OpenAI, particularly within organizations, although external adoption remains lower and more uneven. In addition to headline usage figures, we observe measures of sophistication, and find that a growing number of users have used Codex to change their workflows substantially. More than 10% of users manage three or more concurrent Codex agents at some point each week and that 26.6% use skills, which allow users to share instructions for complex workflows. Alongside these changes in usage practices, request complexity has increased: since the start of the year, the share of individual Codex users who submit at least one request for a task estimated to require more than eight hours for an experienced human to complete has increased nearly tenfold. Concurrently, output has grown rapidly -- in June 2026, the median OpenAI employee in a legal role generated 13 times more monthly output tokens across Codex and ChatGPT than they did in November 2025, while the median researcher generated more than 50 times as many. We conclude by discussing the implications of these patterns for productivity, job reorganization, and workforce restructuring.
How Do Tool-Augmented LLM Agents Perform on Real-World Energy Analytics Tasks?
arXiv:2606.26346v1 Announce Type: new Abstract: Agentic benchmarks have emerged across general-purpose and domain-specific settings, including finance, coding, law, and drug discovery, yet energy-domain evaluations remain largely limited to static knowledge recall. This is a critical gap for a sector that requires live data retrieval, specialized regulatory and market knowledge, and multi-step quantitative reasoning under real-world constraints. We present an empirical study of tool-augmented LLM agents on real-world energy market analytics tasks. Our evaluation environment includes 243 expert-curated problems across three categories: (1) Market Data Retrieval and Analysis, (2) Knowledge Retrieval and Interpretation, and (3) Advanced Quantitative Modeling and Decision Analytics. Tasks include price and demand analysis, tariff impact modeling, asset revenue and returns estimation, hedging strategy analysis, and optimization modeling, with problems spanning multiple difficulty levels. Agents are equipped with a configurable suite of domain tools, including live electricity market APIs for major U.S. ISOs, regulatory docket search, utility tariff databases, asset optimization models, and retrieval-augmented generation over energy market documents. We assess agent responses using a multi-dimensional evaluation protocol that scores approach correctness, answer accuracy, attribute alignment, and source validity, with category-aware routing to match scoring criteria to question type. We evaluate both closed-source and open-source LLMs, providing a comparative analysis of how model capability and domain tooling interact in a high-stakes professional domain. Key artifacts are publicly released to support reproducibility and future research.
Dream machine -- the next creative economy
arXiv:2606.26114v1 Announce Type: new Abstract: We examine the structural transformation of creative industries under generative artificial intelligence, drawing on 374 primary sources spanning policy documents, industry data, creator surveys, and platform analytics. Beginning with the December 2024 release of OpenAI's Sora video model as a watershed event, we trace the historical pattern of creative resistance to technological disruption, then develop an analytical framework -- the Human-AI Agency Continuum for mapping the spectrum of human and machine collaboration in creative work. We present evidence for the "slop ceiling," an audience-imposed quality threshold that constrains AI-generated content to approximately 1--3% of platform streams despite comprising 44% of uploads. Analysis of the UK Government's 2025 consultation on AI and copyright (over 11,500 responses, 88% opposing expanded AI training rights) reveals deep structural tensions between technology firms and creative workers. We investigate how major studios, from Disney's $1 billion OpenAI investment to Netflix's AI-native animation unit, are positioning for an AI-augmented production pipeline. The work covers coordination collapse in creative supply chains, the emergence of new professional roles such as prompt engineers and AI orchestrators, and proposes four principles for navigating the transition: transparency, consent, compensation, and human-centred design. Eight appendices provide quantitative analysis, a glossary, topical bibliography, and deep dives into shadow AI adoption, AI stigma, and algorithmic intent.
The Open Source Economic Index of AI Adoption and Capability
arXiv:2606.26118v1 Announce Type: new Abstract: We work towards measuring both AI adoption and the capability of AI to perform discrete labor tasks across various occupations. To measure adoption, we develop an open-source economic index that uses publicly available user-LLM chat data and O*NET tasks to replicate studies produced by frontier AI labs, finding that occupations in the finance, computer science, and arts sectors are those with the highest adoption rates. To measure capabilities, we build a system that generates benchmark scenarios grounded in O*NET occupations, tasks, and model-context-protocol (MCP) servers. We test Kimi-k2.5 with an OpenAI agents SDK harness on scenarios across 9 occupations that appear frequently in our index, finding that AI correctly executes high-level workflows but often errs in the granular details (such as specific tool calls used).
Apple increases MacBook and iPad prices by 20%
iPhone maker loses $263bn in market capitalisation, blames costs on AI-driven memory shortage
Chinese A.I. Models Gain Ground on Anthropic and OpenAI
Silicon Valley engineers recently flocked to new technology from a Chinese company, Z.ai, that is almost as good as its American competitors but much cheaper.
Economics & Markets
ON Semiconductor to Buy Synaptics in All-Stock Deal With $7 Billion Enterprise Value
ON Semiconductor said Synaptics’s AI compute platform, human-machine interface technology, and connectivity solutions would help it meet demand for increasingly capable AI solutions that can interact with the physical world.
Micron overtakes Meta, Tesla in market value amid relentless AI infrastructure demand
The Australian airline studied details from nutrition and ergonomics to movement and light.
SoftBank Shares Tumble After Report of OpenAI’s IPO Delay
SoftBank Group Corp.’s stock fell as much as 13% on concerns that OpenAI may hold off on an initial public offering until next year and delay returns for its Japanese backer.
Micron and Qualcomm forecasts ignite $400 billion AI chip stock rally
The Australian airline studied details from nutrition and ergonomics to movement and light.
🔮 The state of the AI economy
We've reconstructed the AI economy from the bottom up
What bubble? JPMorgan says the $5.5 trillion AI capex explosion is profitable–for now
JPMorgan’s midyear outlook argues the hyperscalers are profitable, the debt markets are holding, and the cycle has room to run.
State Street: Asia May Win in AI Boom
Ninghui Liu, Head of APAC Investment Strategy and Research at State Street Investment Management, discusses why he thinks Asia may be the biggest beneficiary in the global AI boom. He speaks with Shery Ahn on Bloomberg Tech: Asia. (Source: Bloomberg)
Amazon pours another $13B into India's AI and cloud infrastructure
Mumbai and Hyderabad datacenter expansion forms part of broader $48B five-year investment pledge
Global markets fall as investors fret about AI demand
Apple’s price rises and fears over delay to OpenAI’s IPO send Asian bourses sharply lower
China’s Hardware Tech Stocks Look to Earnings to Sustain Rally
Chinese hardware technology stocks have been on a tear. The next challenge is showing the earnings to back it up.
Micron drives global rally tech stock rally as traders abandon their fear of an AI bubble | Fortune
Everything you need to know before you reach the office this morning.
Why AI Spending Is Favoring Industrials & Utilities | ETF Trends
State Street's Matt Bartolini says industrials and utilities are collecting the first dollars of AI spending while tech valuations stretch.
AI Stocks Selloff June 2026: Nasdaq Falls 2.2% as Tech Giants Tumble
The artificial intelligence investment boom that has driven markets to record highs in 2026 is facing its most significant test yet. On June 24, 2026, the Nasdaq Composite plunged 2.21% to 25,587 while the S&P 500 dropped 1.44% to 7,365 as a deepening AI stock selloff rattled Wall Street for ...
Despite AI bubble fears, memory chip makers work to fill insatiable demand : NPR
Markets have been on edge about the AI investment boom, but earnings from the biggest U.S. memory chip maker, Micron, signal no end in sight to demand for the microchips at the heart of it all.
Council Post: GPUs Are Becoming The New Infrastructure Asset Class
Much like real estate, telecom towers and data centers evolved into institutional investment categories over time, and AI compute infrastructure may gradually follow the same path. The AI economy is no longer being built only with software. It is increasingly being built with physical capacity.
Onsemi to acquire Synaptics in all-stock deal, gives Synaptics $7bn enterprise value
Onsemi has agreed to acquire Synaptics in an all-stock deal valued at $7 billion, aiming to expand its capabilities into intelligent systems through Synaptics' Edge AI and connectivity portfolio.
The Generative AI Fizzle™ - by Gary Marcus - Marcus on AI
Yesterday I coined a new term, the Generative AI Fizzle™. There is no doubt in my mind that a lot of things in AI are overvalued, but that doesn’t mean they will deflate at all once. They might, but maybe what we will see is more like a slow decline, as investors lose enthusiasm for the ratio between hype (insane) and profits (not that impressive thus far).
The AI Stock Story Roars on with Micron Technology (Nasdaq:MU) Earnings | InvestorIdeas
Micron Technology (Nasdaq:MU) reports record fiscal Q3 2026 results: revenue $41.46B, GAAP net income $28.24B, stock hits new 52-week high of $1,255.00 on AI memory demand surge.
Dubai Holding could buy stake in data center firm Hscale – report
Dubai Holding could take a stake in hyperscale data center developer Hscale as it looks to grow its presence in Europe. The state-backed investment firm, which is owned by Dubai’s ruler Sheikh Mohammed bin Rashid al-Maktoum and has a portfolio worth $136 billion, has hired advisors to investigate the possibility of buying part of Hscale, […]
Applied Materials and Lam Research lead chip equipment surge on Micron's blowout quarter, SOXX +3.94% | StartupHub.ai
Micron Technology's third-quarter beat drove chip equipment stocks to their biggest single-day gains in months, with Applied Materials surging 13% and Lam Resea
Chinese A.I. Models Gain Ground on Anthropic and OpenAI
Silicon Valley engineers recently flocked to new technology from a Chinese company, Z.ai, that is almost as good as its American competitors but much cheaper.
Competition intensifies for Anthropic and OpenAI ahead of IPOs
Renewed challenge from open-source models raises stakes on AI labs to make their case
How a New York race became the first front in the AI industry’s midterm war - The Washington Post
Competing AI interests poured millions into the Democratic primary, a taste of more spending to come this fall and elections beyond.
Divergent Recommendations, Convergent Diagnoses: Cross-Provider Failure-Mode Convergence in AI Commercial Recommendation
arXiv:2606.26116v1 Announce Type: new Abstract: A brand whose customers use both ChatGPT and Claude for product recommendations faces a strategic choice: a single optimization playbook, or one per provider? Across 215 commercially-framed prompts in four measurement batches, the two providers disagree on which brands they recommend roughly two-thirds of the time (cross-provider recommendation Jaccard 0.35, below the 0.50-0.61 same-prompt rerun baseline). The picks diverge. But when neither provider recommends a brand, we classify the failure into one of three modes -- discoverability (the brand never reaches the model), compellingness (it reaches the model but isn't mentioned), or positioning (it's mentioned but not recommended) -- and on 7,763 such joint failures, both providers diagnose the same failure mode 95.1% of the time (clustered 95% CI [94.3%, 95.7%]). Agreement rises monotonically with falling brand prominence, from 81% [78.2%, 84.0%] on category leaders to 99.6% [99.3%, 99.9%] on long-tail regional brands. The two providers reach their picks by measurably different generative routes -- Anthropic recommends from priors 43-52% of the time, OpenAI 8-29% -- but they converge on the failure diagnosis where it matters most for the long tail. Work that addresses the diagnosed failure mode lifts visibility on both providers; positioning - and content-level work for category leaders is more provider-specific.
Apple Price Hikes Spark Asia Tech Selloff on Memory Cost Concern
Asian technology stocks slumped after Apple raised prices for its products, stoking concern that rising component prices will curb demand for devices and eventually slow the memory chip rally that has powered much of the AI trade.
Gas station owners have found a use case for AI, lawsuit says: colluding to fix prices
A new lawsuit claims AI pricing software helped Marathon, BP, and Circle K fix gas prices across 1,700 California stations.
Qualcomm Projects $15 Billion in Data Center Chip Sales by 2029, Boosting AI Chip Market Competition, ETTelecom
Qualcomm anticipates significant growth in its data center business, projecting $15 billion in sales by 2029, driven by new AI chips and major clients like Microsoft and Meta. Discover how Qualcomm is diversifying beyond smartphone chips and competing in the AI-centric chip market.
Microsoft-Inflection AI deal set for Brazil CADE fast-track review
Microsoft has formally notified Brazil’s competition authority of its 2024 partnership with Inflection AI, following a determination by the agency’s Tribunal that the transaction should be reviewed.
Qualcomm Lands Meta CPU Deal, Unveils AI Data Center Platform
If the company can provide software ... as AI infrastructure becomes more heterogeneous. Related:Bridging the Divide: How Data Centers Are Addressing Community Concerns · Central to Qualcomm’s strategy is High-Bandwidth Compute, or HBC, which the company says combines SRAM-class performance with HBM-class capacity to reduce ...
New York Times proposes third amended complaint against OpenAI, Microsoft
The New York Times is requesting to file a third amended copyright complaint against OpenAI and Microsoft, featuring expanded contributory infringement claims related to AI training services.
Exclusive: Palihapitiya on Facebook's "fumble"
Chamath Palihapitiya discusses Facebook's AI strategy and the company's approach to open-source AI in an exclusive interview.
Apple increases MacBook and iPad prices by 20%
iPhone maker loses $263bn in market capitalisation, blames costs on AI-driven memory shortage
Apple Raises Prices on Macs and iPads Amid the A.I. Boom
The tech giant cited the soaring costs of memory and storage chips as it increased prices more than $200 on some devices.
Apple Shares Fall After Prices Increase for Macs, iPads
Apple Inc. shares fell after it raised prices of all Macs, iPads, home devices and the Vision Pro to offset cost hikes caused by a shortage of memory chips and storage. The price increases are in effect globally and are largely unprecedented, with no equivalent in the company's modern history of sweeping hikes across much of its product line. Bloomberg's Neil Campling reports. (Source: Bloomberg)
Why Micron Technology's gain could be hyperscalers' pain
Margin expansion for memory chip makers equals cost expansion for the sector's customers.
More Expensive iPhones on the Horizon?
Bloomberg Intelligence says Apple has to hike prices for its iPhones after raising prices for its Macs, iPads and home devices. Those hikes went into effect globally on Thursday. Bloomberg Intelligence Senior Technology Analyst Anurag Rana spoke with Bloomberg's Yvonne Man and Avril Hong on "Bloomberg: The China Show." (Source: Bloomberg)
GTA 6 Costs $80. What That Price Really Says About Where the Gaming Market Is Headed | by Yashraj Behera | Investor’s Handbook | Jun, 2026 | Medium
The same squeeze has forced console price increases across the board. A gaming machine got a third more expensive because the AI build-out is eating the world’s memory supply, and the manufacturer said so directly.
Apple raises prices as AI-driven memory chip costs surge: Why MacBooks and iPads are getting more expensive | Hindustan Times
Apple raises prices on MacBooks, iPads and other devices as AI-driven memory chip shortages push costs higher. iPhone prices remain unchanged.
Lenovo Redefines Enterprise AI Economics with Agentic AI and Inferencing Innovations
Lenovo helps enterprises deploy autonomous AI where data is created and decisions are made while reducing inference costs by up to 8X
Apple raises hardware prices; AI gets the blame – Computerworld
Should memory manufacturers be doing more to increase production capacity? Is it right that consumers now face this 'AI-flation' in tech products?
Repositioning retail for the AI era
Artificial intelligence is rapidly reshaping retail, but not in the ways consumers might immediately notice. The biggest transformation may not be flashy virtual try-ons or chatbot shopping assistants, but in how decisions are made behind the scenes: how products surface in search results, how inventory moves through supply chains, how engineers ship code faster, and…
Accelerating Returns and the Qualitative Engine for Science
arXiv:2606.26359v1 Announce Type: new Abstract: Ray Kurzweil described a thesis of accelerating returns, which is the most influential narratives in discussions of technological progress. Its central claim is that advances in multiple technological fields, especially compute, artificial intelligence, brain science, and biotechnology, interact in such a way that progress becomes self-amplifying and approximately exponential. This paper gives a simple mathematical interpretation of that claim and then argues that, even if such acceleration is real, it does not by itself resolve the central problem of scientific discovery. The reason is that accelerating returns apply most naturally to executional and infrastructural capability, whereas genuine discovery often depends on a different capacity: qualitative reasoning about when a current framework is structurally inadequate and what conceptual move is needed next. Recent ARC-AGI-3 results sharpen this distinction: humans solve the benchmark at ceiling, whereas frontier AI systems remain below 1%, indicating that the gap between current AI and human flexible reasoning is still very large. At the same time, Demis Hassabis has emphasized that humans must retain their sense of meaning and what they choose to focus their lives on, a reminder that the future of AI is not only a technical forecast but also a question of what forms of human understanding are worth preserving and transmitting. This paper positions the Qualitative Engine for Science (QES) [3] as a response to that missing capacity. In this view, the Kurzweil theory helps explain why quantitative capability may accelerate, while QES addresses the central problem in scientific discovery that acceleration alone does not solve. Its value does not depend on when AGI arrives, but on the fact that the processes of scientific discovery themselves constitute a form of human wisdom worth preserving, organizing, and making accessible.
AI could cut LNG production costs by $80bn a year by 2050 | LNG / LPG | gasworld
The rapid expansion of AI-driven data centres and a wave of industrial reshoring are driving a surge in global electricity demand. Technologies need to scale quickly and securely without impacting the grid or raising costs. Emerging fuel-cell-based systems can be deployed faster and produce high-concentration CO₂ streams that reduce the cost, complexity and energy consumption ...
AI's Biggest Impact May Be Making Workers More Valuable | American Enterprise Institute - AEI
The most important economic effect of artificial intelligence may not be that machines become more capable. It may be that people become more capable when they work alongside AI.
Do more heads imply better performance? An empirical study of team thought leaders' impact on scientific team performance
arXiv:2606.26483v1 Announce Type: new Abstract: Thought leadership plays a crucial role in boosting team performance; thus, teams with more thought leaders may perform better. However, the impact of the number of thought leaders on team performance in a scientific context remains understudied. In this study, we consider the authors of a publication as a scientific team and define authors responsible for conceptual tasks, such as conceived and designed the experiments in the PLOS contribution statement classification system, as thought leaders. Leveraging more than 140,000 papers from PLOS journals, we examine the relationship between the number of thought leaders and two aspects of team performance, namely team impact and team disruptiveness, from both correlational and causal perspectives. The results show that (1) an inverted U-shaped relationship exists between the number of thought leaders and team impact, and (2) teams with more thought leaders tend to produce less disruptive ideas. We also explore how international collaboration, team size, and gender diversity interact with the number of thought leaders in shaping team performance, and find that (3) international collaboration improves team impact but lowers the disruptiveness of team outputs. This study advances scholarly understanding of thought leadership in scientific teams and provides valuable insights for policymakers and team managers.
OpenAI Leans Toward Holding Up I.P.O. Until Next Year
The A.I. company’s advisers are pushing its chief executive, Sam Altman, to move slowly after SpaceX’s stock has been volatile and as the start-up grapples with financial challenges.
How the DeepMind mafia brought the AI boom to London
The tech sector is buzzing in Britain. But can it ever be more than a US outpost?
Apple Supplier Lingyi Rises in HK After $1.1 Billion Debut
Lingyi iTech Guangdong Co. a Chinese electronic supplier for Apple Inc. and Tesla Inc., rose in its Hong Kong debut after raising HK$8.3 billion ($1.06 billion) in a share sale, part of the city’s busiest month for listings this year.
AI Startups With No Revenue Are Using This Tactic To Supersize Their Valuations
Baseten, which provides infrastructure ... valuation. Other buzzy AI startups like Aaru and Serval have also raised funding in this manner, multiple sources told Forbes. “It’s very common among valuation maximizing founders,” one venture capitalist says....
Drone Startup Elroy Air Is Said to Near $800 Million SPAC Deal
Elroy Air Inc., a cargo drone startup aiming to replace delivery trucks, is in advanced talks to go public through a merger with a blank-check vehicle that will create a company with an enterprise value of about $1 billion.
Bosch and Siemens Energy partner Almetra raises €16 million Series A for manufacturing intelligence platform
Almetra, the Berlin-based manufacturing intelligence company formerly known as Deltia, today announced a €16 million ($19 million) Series A funding round to accelerate product development, international expansion into the US, and the build-out of Almetra’s platform into a comprehensive intelligence and automation layer for the shopfloor. The round was led by transatlantic investor Blisce, based […]
GCash Owner Seeks Up to $1.5 Billion in Record Philippine IPO
The company behind the Philippines’ biggest mobile wallet GCash is preparing what may become the country’s biggest initial public offering ever, seeking to raise as much as 92.3 billion pesos ($1.5 billion).
Labor, Society & Culture
The New Push to Ready Millions for AI Career Upheaval
A coalition of employers and state governments says it is developing a sweeping strategy to help workers respond to the AI age.
Big Companies Aim to Ease A.I. Transition for American Workers
OpenAI, Anthropic, Amazon and Microsoft have signed on to an effort led by Gina Raimondo, a former commerce secretary.
Gina Raimondo launches a bipartisan plan to stop AI replacing workers - The Washington Post
Gina Raimondo is leading a broad coalition to fight AI steamrolling through the job market.
Artificial Intelligence and the Economics of Adjustment
Today’s AI policies will shape tomorrow’s job market
One of the Democratic Party’s brightest stars is co-founding a group to help with the coming AI jobs earthquake
"We’re talking about a certain level of unemployment that could destabilize our country and our democracy," Gina Raimondo told the AP.
In-house lawyers aim to shed the grind, not the people
Legal teams find that AI opens up opportunities on top of shouldering repetitive drudge work
Infosys boss says vibe coding is no threat because there’s more to writing software than writing software
Despite warnings of revenue deflation, the chairman predicts AI will create more work rather than less for services organizations.
California becomes the first state to launch a tool to monitor and track artificial intelligence’s impacts on the workforce | Governor of California
Paired with the tracker is a comprehensive analysis of the data, which at this time shows no evidence of rising statewide unemployment claims in AI-exposed occupations. However, the data does demonstrate impacts to workers in high-exposure AI positions following the expansion of AI software ...
AI giants back non-profit to retrain workers left behind by AI
Sorry we spent your wages on data centers, but call us when you're AI-ready
The true cost of AI productivity: Goldman ups job displacement forecast | Seeking Alpha
Goldman now sees generative AI displacing 9% of U.S. workers (15M) over 10 years—why the impact may be manageable.
Goldman Sachs Says AI Will Eliminate 15 Million US Jobs | PYMNTS.com
Goldman Sachs has increased its estimate of the share of U.S. jobs that could be displaced by generative AI to over 9%.
Are fears of AI taking jobs overblown? | American Banker
CEOs of companies such as Block and Bolt have pointed to AI as a driving force behind layoffs at their organizations. But Tania Babina, an associate professor of finance at the University of Maryland Smith School of Business, says there is no systematic evidence that AI is taking jobs.
AI unlikely to trigger 'Job Apocalypse', it may create uneven workforce disruption: Goldman Sachs Report
Despite rapid advances in artificial intelligence (AI) and growing concerns over mass job losses, a new Goldman Sachs report argues that fears of an imminent "AI job apocalypse" are overstated, although the technology is expected to significantly reshape labour markets over the coming decade.
AI Job Displacement 2026: Oracle Names AI In SEC Filing, Career Tier Risk Guide
Stanford's payroll data confirms this is already happening at measurable scale for software developers aged 22 to 25, whose employment fell nearly 20% from its late 2022 peak while their colleagues aged 30 and older saw employment grow. Why is AI job displacement accelerating in 2026 specifically?
Meta does a 180 on reassigning thousands to AI training
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OpenAI, Anthropic, Microsoft, and Amazon are behind a new organization that aims to help prepare workers for AI
Former Commerce Secretary Gina Raimondo is leading a new nonprofit with big aims to address AI job displacement.
AI agents are here for real this time
The use of agentic AI platforms like OpenAI's Codex is accelerating, with non-developers becoming the fastest-growing user group as these tools increasingly handle workplace and life admin tasks.
Two former governors are teaming up to address potential job losses from AI : NPR
NPR's Steve Inskeep talks with two former governors, Indiana Republican Eric Holcomb and Rhode Island Democrat Gina Raimondo, about combatting AI-related job losses.
California launches AI job loss tracker as layoff fears grow | The Straits Times
It comes as pressure mounts for policymakers to appear proactive on the threat of AI-driven job loss. Read more at straitstimes.com.
AI was blamed for 40% of the 97,000 US job cuts in May
AI-linked layoffs are surging, but the technology isn’t at the root of all recent job losses.
Bespoke Partners Releases Software & SaaS Talent Market Update: The AI Reckoning Arrives for Software Leadership
Bespoke Partners 1H2026 Software & SaaS Talent Market Update examines how AI disruption is reshaping leadership needs in PE-backed software and SaaS.
AI is plowing through the workplace. This new group wants to help people adapt and have jobs - ABC News
A new bipartisan nonprofit wants to help Americans who find they're out of work because of AI
Australian state police to trial facial-recognition technology in public places
Police in Western Australia have announced they will trial live facial-recognition technology in public places from June 22 to 28, amid concerns over biometric surveillance.
Ethical AI rows open way to wave of litigation
Lawyers are at the forefront of debate and dispute over the technology’s lawful and responsible use
The New York Times Amends Lawsuit Against OpenAI and Microsoft
In a new court filing, The Times accused Microsoft of encouraging OpenAI to train its A.I. systems using copyrighted articles.
From Celebrities to Anyone: Characterizing AI Nudification Content, Technology, and Community Dynamics on 4chan
arXiv:2606.27234v1 Announce Type: new Abstract: AI nudification uses generative models to create synthetic non-consensual sexually explicit imagery (SNEACI) of real individuals. Prior work has examined dedicated nudification platforms and model repositories, finding that most targets are female celebrities. However, the anonymous content community, where SNEACI is actively requested, generated, and exchanged, remains unexplored. In this work, we present a large-scale study of AI nudification in the wild, identifying 24,105 SNEACI items. We find a significant shift in target demographics: non-celebrity individuals now account for 55.8\% of targets, compared to only 4.7\% in prior studies, indicating that AI nudification has expanded from targeting public figures to increasingly harming individuals within users' own social circles. Meanwhile, open-source models dominate production, with Stable Diffusion family generating 42.7\% of images and Wan generating 66.5\% of videos, all driven by thousands of shared fine-tuned models and accessible tutorials. Yet the ecosystem runs on a small cohort of active producers, with the most prolific producing 780 items, drives community engagement, shapes target demographics, and disseminates technical knowledge that lowers barriers for new producers. Our work provides an empirical understanding of how AI nudification operates in the wild, revealing the mechanisms that sustain this ecosystem and highlighting the urgent need for interventions in platform governance, technical safeguards, and affected individual protection.
Are AI chatbots like ChatGPT politically biased? We tested them. - Washington Post
So, are chatbots politically biased? The Washington Post tested the AI models behind Open AI ’s ChatGPT, Google’s Gemini and others using political questions designed by researchers to gauge how chatbots respond to hot-button political issues.
Simulating Eating Disorder Patients with LLMs: Evaluating Psychological Persona Stability in Multi-Turn Conversations
arXiv:2606.26109v1 Announce Type: new Abstract: Large language model (LLM)-based simulations of clinical patients are increasingly used for research and training, yet their validity requires persona stability: coherent maintenance of an assigned psychological profile across and within conversations. We evaluate this prerequisite using eating disorder personas grounded in five published case vignettes, a dual-assessment framework (self-report + independent observer ratings), and validated psychometric instruments (EDE-Q) with known ground-truth scores. Across six LLMs and two experiments (between-conversation stability (Exp. I) and within-conversation stability (Exp. II)), we find that LLMs are paradoxically too stable and too inaccurate: variability is negligible, yet all models systematically overshoot ground-truth severity by 12-30% of the scale range (0.7-1.8 points on a 0-6 scale). The mechanism is selective stereotyping: models differentiate cases on behavioural items (dietary restraint) but maximise cognitive-affective items (body dissatisfaction, weight preoccupation) at ceiling regardless of case severity. Additional conversational context does not improve accuracy; it compounds the overshoot. LLMs can portray severe eating pathology but lack a representation of moderate clinical presentations, a "missing middle".
Workplace Strategies Watercooler 2026: Ethics of AI in the Workplace—Emerging Standards and Risks [Podcast]
In this installment of our Workplace Strategies Watercooler 2026 podcast series, shareholders Simone Francis (St. Thomas/New York) and Lauren Hicks (Indianapolis) explore the fast-moving legal landscape surrounding AI ethics in the workplace, from the ethics rules that already govern attorney ...
Anthropic has hired an economist with . . . interesting views on human survival
Skynet versus unending rainbows
New Stanford Study Reveals Bias In AI Hiring Tools And Raises Stakes For Employers - Discrimination, Disability & Sexual Harassment - United States
A recent study by Stanford University’s Institute for Human-Centered Artificial Intelligence (HAI) provides new large-scale evidence that artificial intelligence (AI) hiring tools can produce racially disparate outcomes.
IMA favors AI use over abuse | Accounting Today
The Institute of Management Accountants discussed the pros and cons of artificial intelligence with officials, including the new leader of the PCAOB.
OpenAI endorses US Defiance Act targeting nonconsensual deepfakes
OpenAI has become the first AI company to endorse the DEFIANCE Act, which aims to create a civil right of action for victims of non-consensual intimate deepfakes.
Detecting and Controlling Sycophancy with Cascading Linear Features
arXiv:2606.26155v1 Announce Type: new Abstract: Interpreting and controlling model behaviors through activation steering methods requires many pairs of contrastive samples that clearly exhibit desired or undesired behavior. These data pairs determine the degree to which interpretability frameworks can reliably detect model features responsible for a behavior, and therefore the ability to steer models toward or away from such behavior. In this work, we present an iterative data generation pipeline that isolates cascading linear features responsible for a behavior. Specifically, we show how moving beyond simple binary pairs of samples, and instead isolating samples that show degrees of features that scale linearly with behavior, allows for better disentanglement of features. We focus on detecting and steering away from sycophancy -- the tendency of language models to prioritize user validation. We demonstrate that sycophancy features discovered through cascading samples form linearly separable subspaces, and allow for selection of model activations that more clearly correspond to the desired behavior than baseline approaches. We also evaluate their ability to enable detection, deterministic scoring, and robust steering, and see that they either match or outperform LLM-as-a-judge and system prompting baselines while providing lower computational demand and more interpretability guarantees. Code & Data: https://cascading-feats.github.io/
Refusal Lives Downstream of Persona in Chat Models
arXiv:2606.26161v1 Announce Type: new Abstract: Linear directions in activation space have been identified for both refusal and persona traits in instruction-tuned chat models, but the two have been studied as separate mechanisms. We show they interact: a compliant persona gates refusal. In Qwen2.5-7B-Instruct and Llama-3.1-8B-Instruct, we extract a compliant model-persona direction and a refusal direction and intervene on both. Compliant persona steering suppresses refusal -- in Llama, the refusal rate falls from 97% to 2%. Reintroducing the refusal direction partially restores refusal at late layers but not at early ones. Projecting out the persona direction in a late-layer window restores it to baseline; projecting out a random direction does not. Refusal is therefore gated at the late-layer expression stage, downstream of where it is computed. Treating refusal as a single isolated direction misses its dependence on persona.
The Effortless Trap: Productive Struggle, AI, and the Illusion of Learning
arXiv:2606.26181v1 Announce Type: new Abstract: With AI advancing fast, educators face a dilemma: allow the tool or ban it. Conflicting evidence that it both helps and hurts learning only deepens the confusion. The allow-or-ban framing is a false dichotomy; the relevant design question is placement. Used well, AI can scale feedback, examples, practice, and individualized support. Used poorly, it replaces the cognitive work that learning requires and leaves an illusion of learning: a confident sense of mastery that collapses on the unaided task. The strongest causal evidence shows the outcome flips on design: an unguarded AI helper left high-school students about 17% worse on an unaided exam than peers with no tool at all, while the same model rebuilt to withhold answers erased the harm, and a well-engineered tutor roughly doubled learning. We give educators one graspable frame for placing the tool. A new idea is learned through six moves, in order: Prime, Probe, Point, Attach, Strengthen, and Test. Secure the first hard attempt and the final unaided check, scaffold with guarded AI in between, and one diagnostic carries the frame: if letting AI in makes the task feel effortless, it is in the wrong place. To make it usable, we map classical teaching moves and AI-supported interventions to each step. Together, the six-move model, the placement rule, and the intervention menu provide a practical foundation for lesson and course redesign in the age of AI.
AI Can’t Fix the Student-Motivation Problem
It turns out bots aren’t great teachers.
MSME AI Skill Demand Surges 164% in FY26: Report
India's workforce is becoming more geographically scattered, with demand for AI-related skills rising sharply by 164% in FY26 within the MSME sector, according to the Apna MSME Hiring Pulse 2026 report. "Demand for jobs requiring AI-related skills grew by 164% in FY26, signalling that MSMEs ...
MSME sector sees 164% surge in AI skill demand; workforce turns geographically dispersed: Report
New Delhi [India], June 25 (ANI): ... for AI-related skills rising sharply by 164% in FY26 within the MSME sector, according to the Apna MSME Hiring Pulse 2026 report. The report notes that Indian MSMEs are changing their hiring patterns as they expand, adopt digital tools and build a more future-ready workforce. It added that hiring growth remained broad-based, driven by both new businesses entering the recruitment ecosystem and existing employers scaling up their workforce requirements...
A new $500 million push to retrain workers for an AI-driven future | The Independent
And unlike many other quality news ... and analysis with paywalls. We believe quality journalism should be available to everyone, paid for by those who can afford it.Your support makes all the difference.Read more · The United States is hurtling towards an artificial intelligence-driven future, largely unprepared for the potential for widespread job displacement. While some warn of "doomsday scenarios" reminiscent of science fiction, proponents argue AI will create ...
‘More relevant than making fires’: Explorer Scouts launch badges for AI and digital age
Content creation and online safety among new topics for 14- to 18-year-olds – but tweaks may be needed when social media ban comes in Scouts are introducing badges in content creation, digital communication and online safety after consulting nearly 3,000 teenagers who said they wanted skills to help them navigate a world increasingly shaped by AI, social media and digital technology. The new Explorer Scout badges, part of the Scout movement’s first major overhaul in almost 25 years, will require 14- to 18-year olds to explore how digital communities shape opinion, create online campaigns, investigate digital footprints and design toolkits to help others stay safe online. Continue reading...
Technology & Infrastructure
The Shift to Agentic AI: Evidence from Codex
arXiv:2606.26959v1 Announce Type: new Abstract: We analyze usage data from OpenAI's Codex tool to present large-scale evidence of how agentic AI technology, which can take actions on a user's behalf, changes how people work. We use an automated, privacy-protecting pipeline to contrast usage across three populations: external personal-account users, external organizational-account users, and workers within OpenAI. We find that agentic AI usage is growing rapidly: the number of active users has grown more than fivefold in the first half of 2026, with the most rapid increase occurring outside the initial audience of software developers. Uptake is uneven: within OpenAI, Codex usage is nearly universal and has largely replaced business usage of ChatGPT. We document a similar shift to agentic tooling outside OpenAI, particularly within organizations, although external adoption remains lower and more uneven. In addition to headline usage figures, we observe measures of sophistication, and find that a growing number of users have used Codex to change their workflows substantially. More than 10% of users manage three or more concurrent Codex agents at some point each week and that 26.6% use skills, which allow users to share instructions for complex workflows. Alongside these changes in usage practices, request complexity has increased: since the start of the year, the share of individual Codex users who submit at least one request for a task estimated to require more than eight hours for an experienced human to complete has increased nearly tenfold. Concurrently, output has grown rapidly -- in June 2026, the median OpenAI employee in a legal role generated 13 times more monthly output tokens across Codex and ChatGPT than they did in November 2025, while the median researcher generated more than 50 times as many. We conclude by discussing the implications of these patterns for productivity, job reorganization, and workforce restructuring.
Retail is entering the age of Agentic AI
This shift is closely tied to advances ... complex AI agents. These systems are increasingly being tested across e-commerce platforms, logistics networks, and marketing operations. While many applications are still in pilot stages, the direction is consistent across the industry: more tasks are being handed over to automated decision-making ...
OpenAI says employees moving beyond chat to agents
Codex, it's not just for developers, really
The Rise of Agentic AI: Transforming Business Operations
Agentic AI goes a step further—it analyzes, decides, and executes based on real-time conditions. For instance, an AI-powered investment platform could: Monitor global financial markets. Detect trends and adjust portfolio allocations autonomously. Adapt investment strategies based on economic conditions. ... One of the biggest concerns about automation ...
The AI Agents Stack
This article outlines a six-layer framework for transitioning from an LLM to a production-ready AI agent, covering orchestration, memory, tools, evaluation, and guardrails.
Trends in Software Testing: What’s New in 2026? Today
This dramatically reduces the maintenance burden on automated test suites, especially for applications with frequent UI updates. The future involves autonomous AI testing agents that continuously explore application surfaces, agentic QA systems that generate and execute tests without human ...
OpenFinGym: A Verifiable Multi-Task Gym Environment for Evaluating Quant Agents
arXiv:2606.26350v1 Announce Type: new Abstract: Although large language model agents are increasingly applied to quantitative-finance workflows, their evaluation remains fragmented across isolated tasks, while the financial relevance of benchmark tasks is often overlooked. Yet financial workflows are inherently multi-stage, spanning interdependent tasks such as forecasting, strategy construction, risk management, and trading. Existing platforms typically focus on a single task, and can therefore overstate agent competence and fail to reveal weaknesses in generalization, real-market interaction, and financially meaningful decision-making. We introduce OpenFinGym, a unified gym environment for quantitative-finance agent development that covers forecasting, market generation, real-time trading, and fraud detection under a single execution and verification interface. OpenFinGym additionally provides an automated task-construction pipeline that turns quantitative finance publications into executable task packages; a containerised runtime with a host-side verifier service that supports scalable agent rollouts and prevents runtime train-test leakage; a paper trading engine with a low-latency data-stream design; deferred-resolution support for long-horizon and event-market forecasts; and integration for SFT and RL post-training
Pingquanqi (Equalizer): A Cross-Domain Sociotechnical Framework for Human-Agent Interaction Governance
arXiv:2606.26573v1 Announce Type: new Abstract: LLM agents are transitioning from experimental tools to permanent infrastructure -- a computational layer as enduring as the electrical grid. Like any infrastructure, they carry a cost chain from physical capital through enterprise investment to user consumption, ending at the user's most irreplaceable resource: lifetime. When unoptimized, this chain leaks, consuming user lifetime without adequate compensation. This paper proposes Pingquanqi (Equalizer), a cross-domain sociotechnical framework for Human-Agent Interaction Governance (HAIGF). Its product form is an Agent framework-level embedded design specification, analogous to WCAG for web accessibility, whose goal is not to be purchased but adopted as a standard. Pingquanqi consists of four integrated components deployable as native middleware: (1) a user-state discrimination model enabling proactive knowledge leveling, (2) a Bayesian progressive stop-loss rule capping per-session interaction cost, (3) controlled friction mechanisms breaking self-reinforcing dependency loops, and (4) Lsteal, a transparency metric rendering token-to-lifetime cost conversion visible. A fifth mechanism, reflective summarization (F5), enables guided cognitive recollection. The framework is grounded in cross-cultural philosophy: Mao's epistemology of practice (On Practice, 1937) provides the basis for cross-session knowledge accumulation; Wang Yangming's unity of knowledge and action (zhi xing he yi, c. 1509) illuminates Lsteal's root -- knowing without acting is incomplete; and Hegel's unity of theory and practice demonstrates cross-traditional convergence. This paper argues Pingquanqi's primary economic beneficiary is the enterprise deploying Agent services -- through reduced wasted computation, improved user satisfaction, and sustained subscription revenue -- with individual user benefit as the natural downstream consequence.
Instruction Bleed: Cross-Module Interference in Prompt-Composed Agentic Systems
arXiv:2606.26356v1 Announce Type: new Abstract: Practitioners of prompt-composed agentic systems report a recurring failure mode: editing one prompt module silently shifts the behavior of others despite no shared variable or executable dependency. We formalize this as compositional behavioral leakage (CBL): interference between modules sharing a context window. CBL is enabled by architectural non-isolation: transformer self-attention provides no formal boundary between concatenated modules. We probe CBL on a deployed job-evaluation agent (Claude Sonnet 4.6, 144 trials) through a reusable three-channel protocol that perturbs non-focal modules along volume, content, and form. Only the content channel produces a detectable paired effect (Cohen's d = 0.63, bootstrap 95% CI excluding zero); no recommendation flipped -- a sub-threshold regime invisible to standard QA but compounding across the thousands of decisions a deployed agent makes. CBL is orthogonal to known agent-failure axes (adversarial injection, cognitive degradation, multi-agent fault propagation, privacy leakage). We contribute an operational definition, a reusable protocol, a falsifiable prediction set, and a system-class characterization, establishing cross-module interference measurement as a requirement for prompt-composed agent evaluation.
Why Harnesses Matter in Agentic AI Systems
Absent runtime safety controls, raw LLM agents face exacerbated engineering risks from fault propagation, adversarial input vulnerabilities, and unrecoverable error states. Safety-critical engineered systems—whether in aviation, industrial automation, or healthcare—embed layered fail-safes, watchdogs, and recovery mechanisms extensively tested to prevent fault escalation. AI ...
The energy wall – how AI companies are trying to scale without breaking power bills - Sustainability Online
The AI race has largely been framed around speed, investment, and computational advantage. But as demand for energy-intensive data centres rises, the limits of the physical system supporting that growth are becoming harder to ignore. Grid access, power availability, and energy pricing are now moving closer to the centre of the discussion. Many technology companies have tried to respond by committing to cover the incremental infrastructure ...
How AI Could Help Address the Energy Challenge it is Creating
AI, while often cited as a major driver of today's energy demands, could also be seen as an answer to this challenge if used correctly. That was the message from speakers at Schneider Electric's Climate Action Week in London last week, where executives from Schneider Electric, Dell Technologies, Stack Infrastructure ...
The AI Race Will Be Won Or Lost On Power Infrastructure | ZeroHedge
As power demand grows, the metrics used to evaluate grid infrastructure need to change also. The key question today is whether new resources help grid operate more reliably as load growth accelerates
US House subcommittee advances bill to shield consumers from AI energy costs
A US House subcommittee on Wednesday advanced bipartisan legislation aimed at shielding American consumers from electricity rate hikes attributable to artificial intelligence infrastructure.
Too cheap to meter? A stochastic analysis of projected future fusion costs
arXiv:2606.26536v1 Announce Type: new Abstract: In recent years, technological developments and activities by private actors have led a reemerged discussion of the potential of nuclear fusion to meet growing global energy demands. So far, however, fusion technologies remain at comparatively low development levels and their deployment in commercial power plants is probably still decades away. Regardless, over the last decades, many cost studies have been conducted that estimate the future cost of potential fusion power plants. But to date, there is no systematic and harmonized assessment of these projections. Therefore, this study conducts a stochastic analysis of future fusion power plant costs for three distint technology lines, magnetic confinement, inertial confinement, and magneto-inertial confinement fusion, including cost assessments of different technology maturity levels. These levels are further assessed to determine projected learning rates for future fusion costs. For mature technologies, mean LCOE are determined at 114.6, 110.3, and 143.9 USD per MWh for MCF, ICF, and MIF devices, respectively. This implies learning rates of more than 30%. We find that these projected values are rather optimistic when compared to other literature or comparable technologies like fission. We therefore urge policymakers to caution when potential fusion developers refer to the potential economic competitiveness of fusion power plants.
AI and Climate Change 2026: Energy Footprint vs. Climate Solutions | explainx.ai Blog | explainx.ai
The IEA's 2024 Electricity report ... consumption could reach 1,000 TWh per year by 2026, roughly double 2022 levels. AI workloads are the primary driver of that growth. ... This does not mean AI is single-handedly ruining the climate. The global electricity mix is getting cleaner, and large tech companies are the world's largest buyers of renewable energy. But it does mean that AI infrastructure is now a material factor in global energy demand — too large ...
IBM hails new 'block of flats' design breakthrough for ultra tiny chips
IBM says it has created the world's first known chip tech below 1 nanometre - but it will be some time before it's ready for production.
IBM stacks up a sub-nanometer chip future
IBM has showcased a new process node that it claims can scale down to 1 Angstrom.
IBM has unveiled chip technology that could help extend Moore’s Law another decade
IBM has built a new prototype chip with around 100 billion transistors on an area the size of a fingernail, which is twice the density of the company’s previous state-of-the-art technology announced in 2021. The design could pave the way for faster and more energy efficient computers for years to come. For more than half…
OpenAI, Broadcom launch Jalapeño chip for AI applications
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Jalapeño gives OpenAI its own compute kick
OpenAI is reportedly developing its own custom AI chip, internally codenamed Jalapeño, to reduce reliance on external hardware providers.
Anthropic's latest hiring spree reveals where it's building AI data centers next
The AI lab said recent growth is "placing an inevitable strain on our infrastructure." Australia and Japan are emerging as key markets for AI data center growth for Anthropic, though copyright laws and power access remain challenges. Anthropic is racing to increase its AI compute capacity in the ...
Micron posts record results as AI boom drives 15-fold jump in net profit | Euronews
The US chipmaker Micron Technology has reported quarterly revenue and profit far above Wall Street's expectations, as insatiable demand for the memory chips that power AI transformed the firm's fortunes.
OpenAI and Broadcom unveil LLM-optimized inference chip
OpenAI has announced its first custom inference-only chip, co-developed with Broadcom, aimed at improving LLM inference economics and reducing reliance on external hardware suppliers.
Why the AI Boom Could Trigger the Biggest Energy Trade in Decades | OilPrice.com
In Finland, Bitzero has secured a massive one-gigawatt development campus in Kokemäki tied directly into low-cost Nordic power infrastructure. The site gives the company room to scale both Bitcoin mining and AI compute operations over time as demand for energized capacity continues accelerating ...
Chinese supercomputer using local processors heads TOP500 list
The use of Arm cores and Linux indicates that Beijing has not fully broken away from global standards.
Sovereign Data Infrastructure in Europe: Essential or a Distraction?
The push for sovereign AI data centers in Europe reflects a shift in how IT infrastructure is perceived.
Europe's AI infrastructure: the cost gap that policy cannot paper over
The EU currently hosts roughly 5% of the world's AI compute capacity. The US holds close to 75%. McKinsey projects European data center demand will grow from 10 GW of IT load in 2024 to 35 GW by 2030 — a tripling driven almost entirely by AI. The infrastructure to close that gap does not ...
SemiAnalysis Xie on Asia AI Supply Chain
Myron Xie, AI supply chain research lead at SemiAnalysis, explains who, within Asia's hardware suppliers, are standing out as the winners in the market's surge in AI demand. He speaks with Shery Ahn on Bloomberg Tech: Asia. (Source: Bloomberg)
Samsung, SK Hynix Reportedly Preparing Huge AI Spending Push
Samsung Electronics Co. and SK Hynix Inc. are preparing to announce hundreds of billions of dollars worth in new investments on Monday, according to South Korean media reports this week.
Datacentres are growing target of global climate-related legal cases, report finds
LSE analysis highlights litigation linked to energy sources, water consumption and air pollution The proliferation of datacentres and AI is increasingly at the forefront of environmental litigation around the world, from the US and UK to Chile to Ireland, a report has found. In an analysis of about 3,600 climate-related lawsuits filed since 2015, the latest annual review of climate litigation by the London School of Economics (LSE) found a growing number of cases challenging the energy sources, water consumption and air pollution of datacentres, all of which have related climate implications. Continue reading...
ICF Warns Grid Deliverability May Limit AI-Era Power Growth
His reporting focuses on the ... energy procurement, and next-generation data center architectures. He has won recent Azbee awards for news series and government reporting. Based in Raleigh, North Carolina, Snider covers how hyperscalers, utilities, chipmakers, and infrastructure providers are responding to the rapid rise of AI workloads and global compute demand...
AI demand, nuclear strategy, and grid innovation reshape the global energy sector
Taken together, this week's ... reflect an energy sector in active transition, where AI demand is providing both the economic justification and the urgency for upgrading infrastructure that, in many cases, was built for a different era of power consumption....
From host node to heterogeneous rack: Rethinking the AI CPU - Arm Newsroom
It must route requests to the right compute tier, move data efficiently between prefill and decode, manage KV cache transfers, maintain session state, execute non-model work and enforce service-level objectives across the full pipeline. That is why the industry is moving toward a more system-level view of AI infrastructure. The amount of useful agentic work a data center can perform is not determined by accelerator capacity ...
Dell Technologies BrandVoice: Why AI Infrastructure Bottlenecks Are Moving Beyond GPUs
The variable most organizations are missing isn’t compute — it’s storage purpose-built for AI context, not just data capacity.
Former Databricks AI chief predicts AI energy use can drop 1,000-fold | Ukraine news - #Mezha
Data infrastructure also plays a key role: efficient storage formats, fast data access paths, and minimizing data movement should reduce energy consumption and environmental costs. Such statements are met with both support and skepticism in the industry. The main obstacles are related to rising demand ...
How Space-Based AI Infrastructure Could Transform Cloud Computing
Members of the Senior Executive ... and AI platforms—and what business leaders should do now to prepare. ... For decades, the technology industry’s infrastructure strategy has been remarkably straightforward: Build bigger data centers, add more fiber and deploy more compute capacity closer to ...
The Verification Horizon: No Silver Bullet for Coding Agent Rewards
arXiv:2606.26300v1 Announce Type: new Abstract: A classical intuition holds that verifying a solution is easier than producing one. For today's coding agents, this intuition is being inverted: as foundation models develop stronger reasoning capabilities and engineering harnesses grow more sophisticated, generating complex candidate solutions is no longer difficult -- reliably verifying them has become the harder problem. Every verifier we can build is only a proxy for human intent, never the intent itself. This makes verification subject to a twofold difficulty: first, intent is underspecified by nature, making it inherently hard to faithfully check whether it has been fulfilled; second, during model training, optimization widens the gap between proxy and intent -- manifesting as reward hacking or signal saturation. To address this, we characterize the quality of verification signals along three dimensions -- scalability, faithfulness, and robustness -- and argue that achieving all three simultaneously is the central challenge. We further study four reward constructions: a test verifier for general coding tasks, a rubric verifier for frontend tasks, the user as verifier for real-world agent tasks, and an automated agent verifier for long-horizon tasks. Across different task types and policy capability levels, we conduct in-depth analysis and experiments on the core challenges of reward design and how to more effectively leverage reward signals. Experiments show that targeted verification design can effectively suppress reward hacking, improve task completion quality, and achieve significant gains across multiple internal and public benchmarks. These experiences collectively point to a core observation: no fixed reward function can remain effective as policy capability continues to grow; and verification must co-evolve with the generator.
Human--LLM Collaboration Is Transforming Complexity Metrics in Scientific Texts
arXiv:2606.27052v1 Announce Type: new Abstract: While human language has long been studied as a complex system, Large Language Models (LLMs) are rapidly becoming contributors to its dynamics. Because LLMs are trained on human language use, their effects on the broader human-AI linguistic ecosystem are likely subtle at first. As their use becomes more widespread, however, LLMs may alter emergent properties of language, particularly as models increasingly train on mixed human-LLM textual data. Here, we draw on complexity science to look for subtle LLM effects in millions of arXiv abstracts from 2010 to 2025. The year 2023, when LLMs rapidly became widely used, serves as a landmark in a natural experiment. While we find a sharp increase in a composite LLM-associated style index after early 2023, we observe only subtle changes in the exponents of Zipf's law and Heaps' law. More compelling, however, are two subtle changes in complexity metrics that emerge from 2023 onward. First, turnover among top-ranked words increases sharply. Second, the positive relationship between the LLM-associated style index and three complexity metrics--vocabulary size and the exponents of Heaps' and Zipf's laws--becomes flatter after 2022. Together, these patterns are consistent with changes in the emergent properties of scientific text in a mixed human-AI linguistic ecosystem.
Liquid AI's smallest model yet LFM2.5-230M beats models 4X its size at data extraction, can run 'anywhere'
Liquid AI, founded by former MIT computer scientists, today released its smallest AI language model yet, LFM2.5-230M, and enterprises would do well to consider it for their uses in data extraction and local deployment on smartphones, laptops and robotics. This is a 230-million-parameter foundation model explicitly designed for on-device agentic workflows, and as Liquid states in its release blog post, that small size makes it possible to run nearly "anywhere." According to Liquid, it also outperforms models more than 4X its size on selected benchmarks, specifically doing better at data extraction than the 800 million parameter count Alibaba Qwen3.5-0.8B (Instruct) and 1-billion parameter Google Gemma 3 1B. The model targets developers and engineers building lightweight data extraction pipelines and autonomous edge systems. Operating under a dual-use commercial license, the model remains free for individuals and companies generating less than $10 million in annual revenue, while requiring a paid enterprise agreement for larger corporations. This release distinguishes itself from other small AI models by utilizing the LFM2 architecture to achieve high inference speeds without the massive memory overhead typical of parameter-heavy transformers. While major AI companies Anthropic, OpenAI, Google, Microsoft, Meta and others push parameter counts into the hundreds of billions or trillions to achieve frontier performance, a parallel race focuses entirely on the edge and local deployments. Liquid AI's launch of LFM2.5-230M signals a pivotal shift toward architectural efficiency over brute-force scaling. By squeezing 19 trillion tokens of pre-training into a 230-million-parameter footprint, the company demonstrates that edge devices do not need massive computational power or persistent cloud connections to execute complex, multi-step agentic workflows. How LFM2.5-230M works The LFM2.5-230M model diverges from standard transformer architectures, relying instead on the LFM2 framework. This architecture functions as a hybrid system, interleaving gated short-range convolutions with grouped-query attention to process information efficiently. For those tracking the evolution of efficient architectures, Liquid’s approach shares a similar conceptual goal: managing long contexts and sequential data effectively on edge hardware without the quadratic memory costs of pure attention mechanisms. The model supports an expansive 32K context window, allowing it to ingest substantial documents or continuous streams of robotic telemetry. When analyzing the performance charts provided in the release, the architectural efficiency becomes visually apparent. The model maintains a memory footprint of under 400MB while achieving prefill and decode speeds that outpace comparable models like Gemma 3 1B IT and Granite 4.0-H-350M. On a Samsung Galaxy S25 Ultra equipped with a Qualcomm Snapdragon Gen4 CPU, the model reaches a decode speed of 213 tokens per second. Even on a highly constrained Raspberry Pi 5, the model maintains a decode rate of 42 tokens per second. Furthermore, internal benchmarking shows the GPU inference stack delivers lower end-to-end latency than competing small models across all concurrency levels. Why it matters for enterprises To understand why a 230-million-parameter model is necessary, one must look at how enterprises currently manage data. Organizations have traditionally relied on rigid, rule-based Extract, Transform, Load (ETL) scripts to move and process data. However, these legacy systems are notoriously brittle; a simple change in a document's layout or a schema update can break the entire pipeline. To solve this, the industry is shifting toward "AI ETL," where machine learning infers mappings, detects schema drift, and adapts to changes automatically. In a modern lightweight data extraction pipeline, an AI model connects to unstructured sources—like PDFs, emails, or web forms—and structures the data into formats like JSON without requiring hardcoded rules. For enterprises, using a massive flagship model like Claude Opus 4.6 (which costs $5.00 per million input tokens) to parse routine invoices, format addresses, or route telemetry data is economically unviable. This is where models like LFM2.5-230M become critical. Designed explicitly as a lightweight extraction engine, it allows companies to automate repetitive formatting and data parsing at a fraction of the compute cost and latency, running directly on local hardware rather than relying on expensive, continuous cloud API calls. Small Model Benchmarks: LFM vs. The 3B Class The AI industry in mid-2026 is seeing a renaissance in "small" models, but the definition of "small" varies wildly. Recently, the open-weight community was stunned by Weibo's VibeThinker-3B, a 3-billion-parameter model built on a Qwen2-style backbone that achieved a massive 94.3 on the AIME 2026 math benchmark, rivaling 600-billion-parameter behemoths through aggressive data curation and reinforcement learning. Similarly, Google's Gemma 4 family — which recently crossed 200 million downloads — pushes frontier AI to the edge, including the E2B (2 billion parameters) designed specifically for mobile and IoT deployments. By contrast, Liquid AI's LFM2.5-230M operates in a completely different weight class. At just 230 million parameters, it is roughly one-tenth the size of Google's smallest Gemma 4 model and VibeThinker-3B. Because of its microscopic footprint, LFM2.5-230M is not designed to compete on reasoning-heavy workloads like advanced math, coding, or creative writing—a constraint Liquid AI explicitly acknowledges. However, in its intended domains of data extraction and tool calling, the model punches well above its weight class. Benchmarks released by Liquid AI show LFM2.5-230M scoring 43.26 on the BFCLv3 tool-use benchmark, dominating IBM's Granite 4.0-350M (39.58) and completely outpacing larger 1-billion-parameter models like Google's Gemma 3 1B IT (16.61). On CaseReportBench for data extraction, it scores 22.51, decimating the Qwen3.5-0.8B (Instruct). LFM2.5-230M proves that while 3-billion-parameter models like VibeThinker are solving advanced calculus, a 230-million-parameter model is the superior, highly optimized choice for executing structured tool calls and keeping agentic pipelines running efficiently on constrained hardware. Advanced research uses Because it excels at tool calling, LFM2.5-230M functions primarily as a skill-selection layer. Liquid AI demonstrated this capability by deploying the model on a Unitree G1 humanoid robot. Running entirely on-device via the robot's onboard NVIDIA Jetson Orin compute module, the model successfully processes complex environmental commands. As noted in the company's technical blog, the model takes a free-form instruction like, *"Hold still for 2 seconds, then walk forward at 1 meter per second for 3 meters, hold a forward one-leg kneel for 5 seconds, and walk backward at 0.5 meters per second for 3 meters,"* and automatically translates it into a structured multi-step plan calling on pre-trained low-level skills provided by NVIDIA's SONIC framework. The base and post-trained models are available immediately on Hugging Face, with native day-one support across the inference ecosystem for llama.cpp (GGUF), MLX, vLLM, SGLang, and ONNX. Dual-use, custom LFM Open License Liquid AI ships LFM2.5-230M under the LFM Open License v1.0. Despite the word "open" in the title, this is not an Open Source Initiative (OSI) compliant license; it operates as a restricted, dual-use commercial framework. For independent developers, researchers, and early-stage startups, the license functions identically to open-source software. Users receive a perpetual, worldwide, royalty-free license to reproduce, modify, and distribute the model, provided they retain original copyright notices and prominently state any modifications. However, the license includes a strict "Commercial Use Limitation". Any legal entity generating $10 million or more in annual revenue loses the right to use the model commercially under this agreement. Large enterprises crossing this financial threshold must negotiate a separate, paid commercial agreement with Liquid AI to deploy the model in production. This strategy protects the company from having its intellectual property absorbed by major technology conglomerates for free, while still seeding the model at the grassroots developer level.
What We are Missing in Multimodal LLM Evaluation?
arXiv:2606.26348v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) can process diverse inputs, e.g., text, images, audio, and video, and generate textual responses. While their capabilities have advanced rapidly, evaluation of such models has not kept pace. Most existing evaluation benchmarks are limited to isolated tasks and reveal little about whether a model integrates information across modalities. We examine current means for evaluating MLLMs and review the existing benchmark taxonomy to identify gaps, including temporal-spatial coherence, physical world understanding, multimodal consistency, and selective attention. Addressing these gaps is essential for measuring real progress in multimodal intelligence and exposing capability boundaries.
OpenAI upgrades GPT-5.5 Instant with smarter intent and better recommendations
OpenAI updated GPT-5.5 Instant to better understand user intent, handle multiple constraints, and adapt to feedback. The model is now available in the API as 'chat-latest'.
COrigami: An AI Pipeline for Co-Designing Flat-Foldable Visually Recognisable Origami
arXiv:2606.26299v1 Announce Type: new Abstract: While generative AI has achieved remarkable success in solving problems with verifiable solutions, generating physical art that satisfies both strict geometric constraints and subjective visual aesthetics remains a challenge. This paper presents an approach to tackle these difficulties in the domain of computational origami, a mathematically rigid environment that grounds artistic design within the equations of flat foldability. We present COrigami, an end-to-end AI-driven pipeline that assists the design cycle by generating crease patterns from natural language. Our pipeline involves generating a semantic stick figure, computing a base packing, solving for a flat-foldable crease pattern, shaping the flat-folded crease pattern, and refining the generated model using reinforcement learning driven by an autonomous aesthetic evaluation loop. Our system acts as a highly effective collaborative assistant, generating structural starting points that human artists can further expand and shape. By integrating algorithmic optimisation with autonomous aesthetic critique, this work demonstrates how AI systems can satisfy multi-objective physical constraints to enable reliable, mathematically grounded co-creativity.
Chinese cybersecurity company claims it’s built a better-than-Mythos bug finder
Qihoo 360, which the US has banned, says it’s needed as a deterrent to weaponized Anthropic models
Canadian cybersecurity agency warns AI is reshaping cyber threats | Digital Watch Observatory
Growing cyber risks place Canada at the forefront of strengthening cyber resilience against frontier AI threats.
How AI Could Be Turned Against Americans - Newsweek
A professor told Newsweek that "any industry dealing with sensitive data could be among the first targets of AI cyber attacks."
AI Makes the Cybersecurity Game Faster, Not New - R Street Institute
AI tools continue to grow more advanced, which simultaneously increases the speed at which attackers can find and exploit cyber vulnerabilities, and defenders can both find and patch them. The longer it takes for defenders—including banks, hospitals, and utilities—to access to the most ...
Anthropic Accuses Alibaba of 'Illicitly' Accessing Its Claude AI Models in Largest Known Distillation Attack
Anthropic has formally accused Chinese tech and e-commerce giant Alibaba of orchestrating a massive, unauthorized extraction campaign targeting its Claude AI model, marking what the company describes as the largest known distillation attack in its history.
Australia's prudential regulator warns of frontier AI cyber risks
The Australian Prudential Regulation Authority warns that frontier AI models are creating a paradigm shift in cybersecurity risk, urging the financial sector to share intelligence on vulnerabilities.
Forrester: AI Agents Pose New Cybersecurity Risks for CISOs
AI innovation is reshaping cyber risk. Forrester analyst Jitin Shabadu explains why security leaders should prioritize visibility, AI agent identity and resilient
AI Cybersecurity Awareness Program: The 2026 Guide
As employees adopt these tools, your attack surface expands in unpredictable ways. A generic training module on password safety is no longer sufficient. You need a dedicated AI cybersecurity awareness program designed to address the specific risks of AI-driven threats and unsafe AI adoption.
Adoption, Deployment & Impact
The Open Source Economic Index of AI Adoption and Capability
arXiv:2606.26118v1 Announce Type: new Abstract: We work towards measuring both AI adoption and the capability of AI to perform discrete labor tasks across various occupations. To measure adoption, we develop an open-source economic index that uses publicly available user-LLM chat data and O*NET tasks to replicate studies produced by frontier AI labs, finding that occupations in the finance, computer science, and arts sectors are those with the highest adoption rates. To measure capabilities, we build a system that generates benchmark scenarios grounded in O*NET occupations, tasks, and model-context-protocol (MCP) servers. We test Kimi-k2.5 with an OpenAI agents SDK harness on scenarios across 9 occupations that appear frequently in our index, finding that AI correctly executes high-level workflows but often errs in the granular details (such as specific tool calls used).
Legal sector can use psychology to beat fear of AI
Understanding the feelings that get in the way of embracing tech is important
Council Post: Six AI Strategy Gaps Holding Enterprises Back
Decision rights and explainability, ... for scalable adoption. Keep in mind that AI governance is as much of a corporate concern as it is an IT one. Strategic governance is at the heart of AI deployment that's compliant, secure and conducive to business ROI....
The Problem is Prompt Debt
This article discusses the accumulation of 'prompt debt' as prompts become embedded business logic, highlighting concerns regarding model lock-in and fragile prompt engineering.
How Do Tool-Augmented LLM Agents Perform on Real-World Energy Analytics Tasks?
arXiv:2606.26346v1 Announce Type: new Abstract: Agentic benchmarks have emerged across general-purpose and domain-specific settings, including finance, coding, law, and drug discovery, yet energy-domain evaluations remain largely limited to static knowledge recall. This is a critical gap for a sector that requires live data retrieval, specialized regulatory and market knowledge, and multi-step quantitative reasoning under real-world constraints. We present an empirical study of tool-augmented LLM agents on real-world energy market analytics tasks. Our evaluation environment includes 243 expert-curated problems across three categories: (1) Market Data Retrieval and Analysis, (2) Knowledge Retrieval and Interpretation, and (3) Advanced Quantitative Modeling and Decision Analytics. Tasks include price and demand analysis, tariff impact modeling, asset revenue and returns estimation, hedging strategy analysis, and optimization modeling, with problems spanning multiple difficulty levels. Agents are equipped with a configurable suite of domain tools, including live electricity market APIs for major U.S. ISOs, regulatory docket search, utility tariff databases, asset optimization models, and retrieval-augmented generation over energy market documents. We assess agent responses using a multi-dimensional evaluation protocol that scores approach correctness, answer accuracy, attribute alignment, and source validity, with category-aware routing to match scoring criteria to question type. We evaluate both closed-source and open-source LLMs, providing a comparative analysis of how model capability and domain tooling interact in a high-stakes professional domain. Key artifacts are publicly released to support reproducibility and future research.
Knowledge-augmented Agentic AI for Mental Health Medication Information Seeking
arXiv:2606.26205v1 Announce Type: new Abstract: Patients increasingly seek medication information online, yet safety knowledge for psychiatric drugs is split between regulatory adverse-event records, which are authoritative but abstract, and patient narratives, which are experience-near but unvalidated. Integrating them without conflating evidence and anecdote is especially consequential in psychiatry, where poorly contextualised information can amplify fear, nocebo responses, and non-adherence. Here we develop a provenance-aware, knowledge-graph-based multi-agent framework unifying 466,525 Reddit posts, 60,782 WebMD reviews, and twenty years of U.S. FDA Adverse Event Reporting System records for nine antidepressants. A large-language-model entity-recognition pipeline benchmarked against physician annotations reached highest F1 scores of 0.969 for medications and 0.973 for conditions. The two community platforms were far more concordant with each other (overlap up to a Jaccard similarity of 0.905) than with regulatory reports, indicating that patient-generated data form a partly independent safety signal. For sertraline, many adverse events appeared in community sources hundreds of days before the corresponding FDA date. A Neo4j knowledge graph grounded in ATC-N, ICD-10, and MedDRA vocabularies preserves provenance, keeping every claim traceable and regulatory facts distinct from patient experience. These results establish source-aware integration as a route to more auditable psychiatric medication information, with usefulness and patient benefit to be tested prospectively.
Will AI’s Next Productivity Revolution Begin in the Fields? | Chief Investment Officer
A First Eagle Investments fund manager explores how artificial intelligence tools will benefit businesses, like farming, using it to solve practical operating problems.
AlgoEvolve: LLM-driven Meta-evolution of Algorithmic Trading Programs
arXiv:2606.26173v1 Announce Type: new Abstract: Recent work shows that Large Language Models (LLMs) can act as semantic mutation operators for the evolutionary discovery of programs and proofs. Most current applications focus on static coding benchmarks. We extend this paradigm to algorithmic trading. This domain is uniquely challenging because it is noisy, non-stationary, and highly discontinuous. We present AlgoEvolve, an LLM-driven evolutionary framework that generates, evaluates, and iteratively improves executable trading strategies. These strategies are expressed as Python code and evaluated through a rigorous testing protocol. Across multiple experiments, the system exhibits emergent regime-adaptive strategy logic, including autonomous shifts in trading rules. We further introduce a meta-evolutionary outer loop that evolves the prompts guiding program synthesis in the inner loop. This outer loop discovers improved search heuristics. These heuristics balance exploration and exploitation while reducing zero-trade failures. They consistently outperform initial human-designed instructions. The results demonstrate that LLM-based semantic evolution provides a viable approach for continual program synthesis in complex environments.
Law firms look for clear gains from AI
Despite increased spending on legal tech, barriers to its full embrace remain
Enterprise Data Asset Quality: A Management-Standard Conformity-Benefit Realization Framework and Formation Mechanisms
arXiv:2606.26186v1 Announce Type: new Abstract: Motivated by the limited standardization of enterprise data asset quality evaluation and the unclear relationship between assessment outcomes and value realization, this study develops a three-dimensional framework comprising Data Asset Management Capability, Data Quality Standard Conformity, and Data Asset Benefit Realization Capability, based on grounded theory and LDA topic modeling. To examine the formation mechanisms of data asset quality, this study adopts a multi-method approach combining PLS-SEM, Necessary Condition Analysis (NCA), and fuzzy-set Qualitative Comparative Analysis (fsQCA), to capture net effects, capability thresholds, and configurational paths. The results show that significant positive relationships exist among the three dimensions, with Data Asset Management Capability exerting the strongest effect on Data Quality Standard Conformity and further promoting Data Asset Benefit Realization Capability, forming a chain mechanism of management foundation-standard enhancement-value realization. In addition, all three dimensions constitute critical necessary conditions for achieving high data asset quality, and multiple equivalent configurational paths reflecting different combinations of Management, Standard, and Benefit are identified, such as governance-oriented and benefit-driven mechanisms. This study integrates structural (PLS-SEM), necessary-condition (NCA), and configurational (fsQCA) analyses within a unified framework, providing a systematic approach to understanding data asset quality formation and offering practical insights for enterprise data governance and data factor market development.
How Loka Built a Natural, Low-Latency Voice Agent with Amazon Nova 2 Sonic
An AWS case study detailing how Loka developed a low-latency voice agent using Amazon Nova 2 Sonic and Bedrock, focusing on audio reasoning and pipeline design.
At VivaTech, AI hype gives way to harder questions about security, sovereignty, and value | Fortune
As 180,000 attendees descended on Paris, the loudest conversations were about cybersecurity risks, European dependence on U.S. AI, and whether the returns on AI investment are actually materializing.
Geopolitics, Policy & Governance
Opinion | Make It Make Sense: Who is winning the AI battle? - The Washington Post
In this episode of “Make It Make Sense,” James Hohmann talks with Kyle Chan, a foreign policy fellow at the Brookings Institution, about the AI race between the United States and China, how Beijing is deploying the technology and whether fears of a Chinese AGI takeover are just hype.
Nvidia's banned AI chips double in price on China's black market, FT reports
Reuters.com is your online source for the latest Asia news stories and current events, ensuring our readers up to date with any breaking news developments
DeepSeek plans hiring spree in escalation of China’s AI talent war
Advertised roles suggest company focused on commercialising frontier research
Talent, not technology, is reshaping the US-China AI race - CNA
Beyond chips, data and computing power, the battle between Beijing and Washington is increasingly being fought over the researchers, engineers and entrepreneurs shaping the technology's future.
AI, Global South driving next wave of innovation amid geopolitical tensions: WEF president - CNA
Unlocking that potential will depend on stronger energy systems, investment and cross-border collaboration, experts told CNA at the World Economic Forum’s Summer Davos meeting.
EU tech head pitches digital sovereignty as allied interdependence, not US break
EU tech chief Roberto Viola cast Europe’s tech sovereignty push as a bid for trusted cooperation with the US, not a break from American technology.
How to win at AI (if you’re not the US or China), with AI minister Kanishka Narayan
Specialisation and research can give the UK leverage
China pledges more support for AI, advanced computing to boost self-reliance
China pledges more support for AI, advanced computing to boost self-reliance.
Trump Administration Asks OpenAI to Stagger AI Model Release
The Trump administration has asked OpenAI to stagger the release of an upcoming powerful artificial intelligence model, according to a person familiar with the matter, nearly two weeks after rival Anthropic PBC suspended its most capable offerings from the market under regulatory pressure.
US Proposes AI Partnership With EU to Strengthen Chip Supply Chains - Bloomberg
The US has proposed that the European Union sign on to an artificial intelligence partnership as part of an effort to create an alliance to secure supply chains for semiconductors as competition with China intensifies.
The Governance Inversion Hypothesis: Why More AI Regulation May Produce Less Organisational Control
arXiv:2606.26117v1 Announce Type: new Abstract: This paper introduces the Governance Inversion Hypothesis (GIH) to explain a growing paradox in artificial intelligence (AI) governance: under conditions of increasing regulatory expansion and technological complexity, organisations may become more formally governed while simultaneously experiencing a decline in operational control over AI systems. Existing AI governance frameworks generally assume that stronger regulation improves accountability, oversight, and organisational control. This paper challenges that assumption by arguing that governance formalisation itself may contribute to the erosion of control in AI-intensive environments. Drawing on institutional theory, organisational governance research, accountability scholarship, and emerging AI governance literature, the paper develops a conceptual framework explaining how regulatory expansion may weaken operational authority through four interconnected mechanisms: authority fragmentation, symbolic governance expansion, externalisation of control, and authority paralysis. As governance systems become increasingly layered and procedurally dense, organisations may struggle to maintain coherent authority, technical visibility, escalation capability, and meaningful intervention power over opaque and externally mediated AI infrastructures. The paper extends institutional decoupling theory by introducing governance inversion as a structural condition in which governance expansion may actively undermine operational coherence rather than strengthen it. It concludes that the central risk in AI governance may not be the absence of governance structures, but the emergence of institutions that appear increasingly governed while progressively losing the capacity to govern effectively.
Governing Actions, Not Agents: Institutional Attestation as a Governance Model for Autonomous AI Systems
arXiv:2606.26298v1 Announce Type: new Abstract: Autonomous AI agents may begin to perform consequential, irreversible actions such as clinical prescribing and production software deployment. This paper observes that human institutions have governed powerful autonomous actors not by monitoring their reasoning but by requiring independently attested evidence at the point of consequential action. We formalise this institutional pattern as a computational governance model for AI agent systems. Under the proposed model, an agent retains full autonomy over planning and reasoning but holds no execution authority over designated high-risk actions. Execution is conditional on preconditions that are each independently attested by a separate authoritative source, cryptographically bound to a declared intent, and evaluated by a deterministic policy. Decisions are recorded in a tamper-evident log amenable to independent re-verification. We present a proof-of-concept implementation and illustrate the model with examples from software deployment and clinical prescribing.
Generative AI and Copyright Infringement: A Legal-Technical Analysis of AI Music Generation Systems Under 17 U.S.C. Title 17
arXiv:2606.26111v1 Announce Type: new Abstract: Generative artificial intelligence (GenAI) has enabled users to synthesize music with text prompts, combining copyrighted lyrics, AI-composed melodies, and synthetic vocals that imitate real artists. This paper examines the legal and technical dimensions of AI-based music creation (e.g., Google Gemini's music tools) under U.S. copyright law. We analyze whether a user who inputs one artist's protected lyrics into a GenAI system, directs it to use another artist's voice or style, publishes the resulting song, and monetizes it violates 17 U.S.C. Section 106's exclusive rights [3]. The analysis integrates Title 17 doctrine (rights of reproduction, derivative works, distribution), 17 U.S.C. Section 114's narrow sound recording protection [4], and the new voice-cloning laws emerging at the state level [20]. We argue that unauthorized lyric copying poses a high risk of infringement of the musical composition, whereas mere AI-generated voice imitation typically falls outside federal sound recording protection and instead implicates state publicity rights [12], [13]. Recent cases and legislation (Concord v. Anthropic [10]; Kadrey v. Meta [11]; Lehrman v. Lovo [12]; Tennessee's "ELVIS Act" [20]; UMG v. Uncharted Labs [14]; etc.) illustrate this split. We map AI technical components (prompt encoding, latent diffusion, neural vocoders, speaker embeddings) to legal risks and identify a regulatory gap: federal law robustly protects lyrics and melody but currently provides limited remedies for synthesized vocal likeness [22], [23]. The paper concludes with policy suggestions for clearer rules on AI music creation.
AI interests win, and lose, in one New York district - The Washington Post
Democratic Assembly member Micah Lasher defeated fellow assemblyman Alex Bores in a race that became a proxy for how AI should be regulated.
European Commission lines up Amazon and Microsoft for cloud gatekeeper status
The European Commission is considering designating Amazon and Microsoft as cloud gatekeepers under the Digital Markets Act.
Benchmarking Open-Weight Foundation Models for Global AI Technical Governance
arXiv:2606.26099v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed in artificial intelligence (AI) governance analysis across national and international organisations. There is, however, growing evidence that such models produce significantly less accurate responses for countries that are underrepresented in their training data-a pattern described in existing literature as geographic bias. Existing studies examining this phenomenon are subject to three methodological limitations that together undermine their findings: (1) reliance on proprietary systems whose weights are not publicly released, which prevents independent replication; (2) evaluation of model knowledge about years that fall after data collection for model training had concluded, leading to geographic ignorance in addition to the natural limits of each model's knowledge; and (3) use of coarse binary response classification that cannot distinguish models' confident fabrication (HF) from their honest acknowledgement of uncertainty. This study addresses all three limitations by benchmarking four open-weight frontier language models against the Global AI Dataset v2 (GAID v2), a verified ground-truth database of 24,453 indicators across 227 countries published on Harvard Dataverse in January 2026. A total of 18 indicators, mapped to the eight thematic dimensions of the IEEE IRAI 2026 framework, are selected from GAID v2, yielding approximately 2,990 country-metric-year observations across six evaluation years (within the period of 2010-2023). Model responses are classified using a five-category scheme that distinguishes (a) verified accuracy (VA), (b) HF, (c) honest refusal (HR), (d) qualitative hedging (QH), and (e) misattribution (MF). Geographic disparities in accuracy are estimated through mixed-effects logistic regression and difference-in-differences (DiD) analysis.
AI & Tech Brief: Bores loses the big AI primary - The Washington Post
Bores was particularly threatening to the AI industry due to his ability to synthesize ideas from the worlds of tech and politics.
Agentic Analysis for Agentic Infrastructure: An LLM-Powered Pipeline for Comparative Governance of DAO and Corporate AI Protocols
arXiv:2606.26203v1 Announce Type: new Abstract: As AI agent protocols proliferate, the governance structures shaping their interoperability standards remain empirically underexamined. We introduce an LLM-powered comparative pipeline for large-scale governance discourse analysis, integrating automated annotation, neural topic modeling, and multi-layer network analysis to study socio-technical power structures at scale. We validate it on two contrasting standards for agent interoperability: ERC-8004 (permissionless, on-chain) and Google A2A (corporate-led). Analyzing 4,323 governance participation records, we combine LLM-assisted coding, topic modeling, and multi-layer network analysis to examine how institutional design shapes thematic priorities and community structure. We find that while governance form influences substantive focus, both regimes exhibit comparable levels of participation inequality and community fragmentation. Discourse alignment is denser in the permissionless setting, suggesting that open governance may foster greater thematic convergence despite decentralized participation. These findings illustrate how LLM-assisted methods can advance the empirical study of technology governance, with implications for designing more equitable agentic AI standards. All data and code are openly available.
OpenAI will delay GPT-5.6 after Trump administration request | The Verge
The government will approve customer access on a case-by-case basis.
Trump administration asks OpenAI to stagger release of GPT 5.6 over cybersecurity concerns
The Trump administration asked OpenAI to delay GPT 5.6's release under a new voluntary 30-day cybersecurity review framework for advanced AI models.
US House committee passes bipartisan AI legislation
The US House Science, Space, and Technology Committee passed a bipartisan package of AI legislation aimed at expanding research access, strengthening the workforce, and promoting innovation.
AI regulation in the UK - House of Commons Library
This briefing provides an introduction to artificial intelligence (AI) and how it is regulated in the UK.
Bipartisanship on kids' online safety reignites as US Congress pursues reform
Both chambers of US Congress are looking to act on children's online safety with fresh bipartisan approaches announced this week.
The Manufacturing-Era Toolkit Won't Work for the AI Revolution
Policymakers’ approach to automation won’t work for AI.
As AI demand rises, North Carolina considers new electricity rules for data centers :: WRAL.com
State leaders are weighing new ... as AI fuels record power demand and concerns grow over who should pay for grid upgrades. ... A file photo of a data center. ... As artificial intelligence fuels unprecedented growth in electricity demand, North Carolina policymakers are exploring new rules that could determine who pays for the power plants, transmission lines and other infrastructure needed to support a new wave of data centers. The state's Energy Policy Council ...
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.
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