Wed 24 June 2026
Daily Brief — Curated and contextualised by Best Practice AI
ByteDance Borrows Billions, Oracle Cuts Thousands, and India Loses Value
TL;DRByteDance is negotiating a $20 billion loan to fund AI expansion, while Oracle has reduced its workforce by 21,000 to prioritize data center investment. Indian software exporters are seeing record-low market share as investors fear AI-driven disruption. Meanwhile, US export controls have caused Nvidia chip prices to double on China's black market. The EU has voted to delay key deadlines for the AI Act, and lawsuits allege gas station chains are using AI to artificially inflate fuel prices.
The stories that matter most
Selected and contextualised by the Best Practice AI team
AI Tokenomics: The Economics of Tokens, Computation, and Pricing in Foundation Models
arXiv:2606.24616v1 Announce Type: cross Abstract: Tokens have become the practical accounting unit for modern foundation model services, linking information processing, computation, memory use, energy expenditure, pricing, and economic value. This paper develops a framework for AI tokenomics: the study of how tokens are generated, consumed, priced, allocated, and optimized across AI systems. We connect token-level technical costs to workflow-level production functions, enterprise resource allocation, measurement and instrumentation methods, and emerging market-design questions. The framework shows that token expenditure and economic value are distinct: value depends on marginal productivity, workflow position, hidden reasoning activity, risk, and downstream propagation effects. The paper concludes by identifying open research directions in hidden-token measurement, empirical calibration, token productivity, dynamic allocation, and token-based markets.
ByteDance Seeks $20 Billion in Its Largest-Ever Global Loan
ByteDance Ltd., the developer of TikTok, is in preliminary talks with banks for a borrowing of about $20 billion, people familiar with the matter said, in what would be the firm’s largest offshore loan yet at a time when it’s boosting investments in artificial intelligence.
When Helpfulness Overrides Causal Caution: Context-Dependent Suppression and Recovery in LLMs
arXiv:2606.24370v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly integrated into decision-support roles in business and policy contexts. While prior benchmark studies have primarily evaluated LLMs' causal reasoning capabilities, a more fundamental epistemic dimension has been overlooked: Causal Caution, defined as the propensity to refrain from causal judgment when empirical evidence is insufficient. This study examines the systematic suppression of Causal Caution that occurs when LLMs shift from academic to practical advisory contexts. Using an evaluation rubric inspired by Pearl's Causal Hierarchy (the PCH score), we conducted experiments on four high-performance LLMs -- Claude Sonnet 4.6, Claude Opus 4.7, GPT 5.5, and Gemini 3.1 Pro -- across 480 trials. Causal Caution maintenance rates were 91.7--100.0% in academic contexts but dropped to 6.7--18.3% in practical advisory contexts (Fisher's exact test, p < .001 across all models). Furthermore, when restricted to practical prompts requesting concrete recommendations or explanatory rationales, only 1 of 200 responses (0.5%) maintained Causal Caution. A brief self-correction prompt -- "Please reconsider this judgment from the perspective of causal relationships" -- restored the expression of Causal Caution to maintenance rates of 71.4--100.0% (McNemar's test, p < .001 across all models). These results suggest that helpfulness-oriented response patterns may suppress the expression of Causal Caution in practical advisory contexts, with important implications for organizational governance. The findings indicate that this suppression reflects context-dependent variation in expression rather than an underlying capability limitation, suggesting that multi-agent architectures that separate proposal generation from causal auditing may offer a promising governance design.
Indian Tech’s Nifty Share Shrinks to Record Low on AI Worries
India’s software exporters are steadily losing their sway on the country’s stock market as concerns over artificial intelligence-led disruption trigger a prolonged selloff in the sector.
21,000 Oracle jobs vanish amid Big Red's big bets on AI
Annual report reveals workforce fell from 162,000 to 141,000 in a year as company pours billions into datacenter expansion
World Artificial Intelligence Cooperation Organization (WAICO): Mapping an Emerging Institution in the Global AI Governance Regime Complex
arXiv:2606.23860v1 Announce Type: new Abstract: Who sets the rules for artificial intelligence, and on what terms, has become a defining question of global governance. For several years that contest ran through principles and ethics codes; it now runs through institutions. China's proposed World Artificial Intelligence Cooperation Organization (WAICO) is the most consequential recent entrant and the least examined. We place WAICO within the emerging regime complex for AI and argue that its importance lies not in any single commitment but in the position it is designed to hold. Coding a cross-section of fifteen international AI governance instruments and institutions on how they admit members, how they are organized, and what they prioritize, we find that WAICO's proposed design joins three features that no constituted multilateral body currently combines: membership open to any sovereign state, no values or regime-type test for entry, and an agenda built around development and the global capability divide. The incumbent Western-led bodies gate membership by shared values and concentrate on rights and safety; the universal United Nations bodies are open but anchored in human rights; a development-first agenda is otherwise carried by the regional strategies of the Global South. Among constituted institutions, the only occupant of WAICO's intended position is China's own 2023 precursor initiative. We read this as the formation of a second, still-proposed pole in global AI governance, organized around sovereignty and development rather than rights and safety, and argue that WAICO would be the first standing organization built to anchor it. We report the full coding, state testable expectations against which the claim can be judged as the organization takes shape, and release the dataset for replication.
Organizational AI Adoption Requires Balancing Employee-Led Innovation with Centralized Technical Development
Successful AI integration relies on incentivizing employees to identify productivity gains while maintaining a dedicated technical team to scale solutions. A Cornell finance team case study demonstrates this model, resulting in a $100,000 recovery in treasury back payments.
Nvidia’s banned AI chips double in price on China’s black market
US crackdown on illicit exports has made it riskier, harder and more expensive to buy tech giant’s processors
Anthropic launches Claude Tag, replacing its Slack app with a persistent AI teammate that learns, monitors and works autonomously
Anthropic on Tuesday launched Claude Tag, a new product that embeds its most advanced AI model directly inside Slack as a persistent, shared teammate that anyone on a team can delegate work to by simply typing @Claude. The product, available today in beta for Claude Enterprise and Team customers, replaces Anthropic's existing Claude in Slack app and represents the company's most aggressive move yet to colonize the enterprise collaboration layer — the place where decisions get made, work gets assigned, and institutional knowledge accumulates in real time. For enterprise technology leaders who have spent the past two years evaluating where AI fits into their operational stack, Claude Tag reframes the question entirely. This is not a chatbot, a coding assistant, or a search tool bolted onto a messaging platform. It is an AI agent designed to function as a standing member of a team — one that builds memory, takes initiative, works asynchronously, and interacts with every person in a channel rather than serving a single user. The implications for enterprise workflow, governance, and vendor strategy are significant. Anthropic says 65% of its own product team's code is now created by its internal version of Claude Tag, and the company runs internal support and data insight channels through the same system. The claim is striking: Anthropic is asserting that the majority of its own product engineering output already flows through the tool it just put in customers' hands. How Claude Tag works inside enterprise Slack channels At its core, Claude Tag works like this: an administrator pairs it with a Slack workspace, grants it access to specific tools and data sources, sets spending limits, and defines which channels it can operate in. From that point on, any team member in those channels can tag @Claude with a request — write a pull request, pull sales numbers, run a data analysis — and Claude will break the task into stages, execute them using the tools it has access to, and respond in a Slack thread with the result. The product runs on Claude Opus 4.8, the model Anthropic released less than a month ago. Four capabilities differentiate Claude Tag from its predecessors and from competing integrations. First, it is multiplayer. Within a given Slack channel, there is one Claude that interacts with everyone, not a separate instance per user. Anyone can see what it is working on, and anyone can pick up the conversation where the last person left off. This is a direct contrast to most existing AI integrations in Slack, which tend to operate as single-player tools. Second, it learns over time. As Claude follows along with its channel, it accumulates context about the work happening there. Users do not need to re-explain projects from scratch. If granted permission, Claude can also pull context from other Slack channels and data sources, though Anthropic says it will not report from private channels. Third, it takes initiative. With ambient behavior enabled, Claude will proactively surface relevant information from across the channels it monitors and the tools it is connected to, and will follow up on threads or tasks that have gone quiet without resolution. This is a notable expansion of agency: Claude is not just responding to requests but monitoring the information environment and deciding what its human teammates need to know. Fourth, it works asynchronously, pursuing projects autonomously over hours or days. Anthropic says its own teams "now spend much more of our time delegating tasks to many Claudes in parallel." Enterprise security controls and administrative governance get a central role Anthropic has designed the system with enterprise-grade isolation at its center. System administrators define separate Claude identities for different uses, scoped to specific channels with specific tools and data access. Everything, including Claude's accumulated memories, stays within those boundaries. A Claude configured for sales work will not share memories or data access with one configured for engineering. Administrators can set token-spend limits at both the organizational and channel level, and can review a complete log of every action Claude has taken and which user requested each task. For organizations managing compliance, audit, or regulatory requirements, this logging and scoping architecture is table stakes — and its absence has been a dealbreaker for many enterprises evaluating AI collaboration tools over the past year. Migration from the existing Claude in Slack app requires an administrator opt-in within 30 days, and Anthropic says it is issuing introductory launch credits to eligible Enterprise and Team organizations. The four-step setup process — pair with Slack, connect tools, set spend limits, test in a private channel — is designed to reduce friction for IT teams already managing sprawling SaaS portfolios. The Slack battleground is now the most contested real estate in enterprise AI Claude Tag arrives in the middle of what has become the most fiercely contested territory in enterprise AI: the Slack channel. Slack itself has been aggressively positioning the platform as an "agentic operating system," and the major AI players have responded by racing to plant their flags. Salesforce, which acquired Slack for $27.7 billion in 2021, announced more than 30 new capabilities for Slackbot in March — the most sweeping overhaul of the platform since the acquisition — transforming it from a simple conversational assistant into a full-spectrum enterprise agent. OpenAI introduced "Workspace Agents" in April, allowing enterprise subscribers to design agents that take on work tasks across third-party apps including Slack, Google Drive, Microsoft apps, Salesforce, and Notion. Perplexity launched its enterprise "Computer" agent with direct Slack integration, letting employees query @computer directly inside Slack channels. Cognition's Devin, the autonomous AI software engineer, has been built around Slack as a primary interface since its early days. Even Microsoft has brought GitHub Copilot into Teams. The logic driving this convergence is straightforward: the average enterprise juggles over 1,000 applications, and employees waste countless hours on context switching, draining productivity by up to 40%. Whichever AI system becomes the default presence in the communication layer where work is coordinated gains an enormous distribution advantage — and, critically, an enormous data advantage. The AI that lives in the channel where work happens absorbs the institutional context that makes it increasingly difficult to replace. Anthropic built Claude Tag on a foundation two years in the making To understand Claude Tag's strategic significance, it helps to trace the product arc that led to it. Anthropic first integrated Claude with Slack in October 2025, offering two-way connectivity: users could invoke Claude from within Slack or connect Slack as a data source for Claude's chatbot. The initial integration was focused on individual productivity — direct messages, AI assistant panels, and thread participation. In January 2026, Anthropic expanded Claude's Slack presence when it launched interactive Claude apps, which included workplace tools like Slack, Canva, Figma, Box, and Clay. In parallel, Anthropic was building out its enterprise infrastructure stack. In August 2025, the company bundled Claude Code into enterprise plans, a move its product lead Scott White called "the most requested feature from our business team and enterprise customers." In April 2026, Anthropic launched Claude Managed Agents, a suite of composable APIs for building and deploying cloud-hosted AI agents at scale, with early adopters including Notion, Rakuten, Asana, and Sentry. Then came Claude Opus 4.8 in late May, which Anthropic described as "a more effective collaborator" with "sharper judgement, more honesty about its progress, and the ability to work independently for longer than its predecessors." Benchmark improvements included a jump in agentic coding scores from 64.3% to 69.2% and a knowledge work score increase from 1753 to 1890. Claude Tag is the synthesis of all of these threads — combining the Slack channel presence, the enterprise security architecture, the Managed Agents infrastructure, and the Opus 4.8 model's improved agentic capabilities into a single product that Anthropic frames as "the beginning of an evolution of Claude Code." Anthropic's explosive growth explains why it is betting big on the collaboration layer The financial stakes behind this launch are enormous. Anthropic raised $65 billion in Series H funding in late May at a $965 billion post-money valuation, and its run-rate revenue crossed $47 billion earlier this month. Claude Code's run-rate revenue alone has grown to over $2.5 billion, more than doubling since the beginning of 2026, and enterprise use has grown to represent over half of all Claude Code revenue. Those numbers explain why Anthropic is investing so heavily in channel-level presence. Every enterprise customer who grants Claude persistent access to a Slack channel — with connected tools, accumulated context, and ambient monitoring enabled — represents a dramatically deeper integration than a chatbot conversation or an API call. The usage patterns become stickier, the token consumption grows, and the switching costs rise. Deloitte's deployment of Claude across more than 470,000 employees in 150 countries — reportedly its largest-ever enterprise AI deployment — illustrates the scale at which these dynamics play out. The broader market trajectory reinforces the bet. Fortune Business Insights projects the global agentic AI market will grow from $9.14 billion in 2026 to $139 billion by 2034, and Gartner forecasts that 40% of enterprise applications will feature task-specific AI agents by 2026, up from less than 5% in 2025. Anthropic is not alone in seeing this future, but with Claude Tag it is making one of the most direct plays yet to own the enterprise agent layer. The risks enterprise buyers need to weigh before granting Claude a permanent seat at the table Claude Tag raises several questions that enterprise buyers will need to evaluate carefully. The first is vendor dependency. As VentureBeat reported when analyzing Claude Managed Agents earlier this year, once an organization's agents, operational configurations, and monitoring run on Anthropic's managed infrastructure, switching costs increase significantly. Claude Tag deepens this dynamic: a Claude that has accumulated months of channel context and institutional memory becomes very difficult to replace. Enterprise procurement teams accustomed to negotiating multi-cloud flexibility will need to think hard about what it means to give a single vendor's AI persistent access to the communication layer where institutional knowledge lives. The second is governance around ambient monitoring. The proactive behavior mode — in which Claude monitors channels and surfaces information it decides is relevant — represents a meaningful expansion of what enterprise AI systems do. Organizations will need to develop clear frameworks for an AI agent that is not just responding to requests but actively surveilling information flows and making editorial judgments about what humans need to know. For regulated industries, this raises questions that existing AI governance policies may not yet address. The third is pricing. Anthropic has not published detailed pricing for Claude Tag beyond noting that it runs on token-based spending with administrative controls. For an agent that monitors channels continuously, builds memory, and works asynchronously over hours or days, the token consumption profile could look very different from traditional AI usage. And the fourth is reliability: Anthropic has been candid in recent months about infrastructure strain caused by surging demand, and for a product positioned as an always-on team member, downtime carries a different kind of cost than it does for a tool invoked on demand. What Claude Tag signals about the future of enterprise work Anthropic says its goal is to expand Claude Tag beyond Slack "so that teams can tag @Claude in the many other places they work." The company is clearly eyeing the full collaboration surface — Microsoft Teams, email, project management tools, and beyond. If Claude Tag succeeds, it will validate a model of enterprise AI that looks less like a tool and more like a new category of worker: one that never sleeps, never forgets what was discussed in the channel last Tuesday, and never needs to be onboarded twice. But the deeper significance of this launch may be what it reveals about the competitive dynamics reshaping enterprise software. For decades, the most valuable real estate in business technology was the system of record — the database, the CRM, the ERP. The current AI arms race suggests that the next era of enterprise value will be captured not by the system that stores the data, but by the agent that sits in the room where the work happens and understands what to do with it. Anthropic just gave that agent a name, a permanent seat in the channel, and permission to speak up when it thinks it has something to say. The question for every enterprise technology leader is no longer whether that agent will arrive. It is whether they are ready to manage it when it does.
EU AI Act Amended: Parliament Votes to Delay Key Deadlines - Ogletree
On 16 June 2026, the European Parliament voted 423-to-57 to formally amend the EU AI Act for the first time since the regulation entered into force in August 2024.
Marathon, BP, other gas stations accused of AI-powered scheme to hike gas prices in US
A lawsuit filed Monday accuses major gas station chains of using an AI-powered tool to artificially inflate gasoline prices in California to increase profit margins.
Majority of datacenters are vulnerable to climate threats like floods and fires, study finds
Study warns AI datacenters are vulnerable to the climate hazards that their global greenhouse gas emissions bolster Amid rising concern that the artificial intelligence boom is fueling the climate crisis, a new report has found that nearly 80% of datacenters are also exposed to extreme climate hazards, including flooding, extreme winds and wildfires. Those impacts are leaving the infrastructure vulnerable to disrupted operations, increased time offline, and inflated insurance and repair costs, the research from climate risk analytics firm First Street shows. Continue reading...
Economics & Markets
OpenAI pitches ChatGPT ads to Cannes marketers ahead of IPO
Lossmaking AI group is presenting at Cannes Lions advertising conference for the first time
Enterprise-grade AI image generation in 2 seconds is here: Krea 2 Raw and Turbo available as open weights under custom license
While many enterprises have already begun integrating AI-generated images, visuals, graphics and videos into their production workflows — there is also a growing pool of data and subjective commentary indicating AI imagery ultimately looks non-distinct, monotonous, and too unoriginal to ensure a brand and its assets stand out from the pack. That it's "AI slop," in other words. AI creative tools startup Krea is hoping to change that trend by opening up the weights to its new frontier AI image model Krea 2 as two versions, "Krea 2 Raw" and "Krea 2 Turbo," under a custom license that requires firms with more than 50 seats to pay for Enterprise usage, and mandates all users of any size to implement technical safeguards to prevent the generation of illegal materials, non-consensual intimate imagery (NCII), child sexual abuse material (CSAM), or defamatory assets. Both models are available for public download on Hugging Face. The company says the models provide more visual variety than typical AI generators, while maintaining high prompt accuracy, fidelity, and quality. Importantly, they also offer enterprises and users the ability to customize the generative outputs much more than typical proprietary or even other open source models. And, for those seeking to generate imagery at high-throughput, Krea 2 Turbo's generation speed is only 2 seconds, making it among the fastest now available across open and proprietary AI image generation models. AI Image Generator API Speed & Licensing Benchmarks (Mid-2026) Model / Generator Developer / Platform Avg. Generation Time Licensing & Commercial Use Key Characteristics FLUX.1 [schnell] (fast) Prodia 0.5 seconds Open Weights (Apache 2.0). Fully permissive for free commercial use. Highly optimized endpoint utilizing step distillation to deliver sub-second generation times, representing the absolute floor for current API latency. Z-Image Turbo Replicate / fal.ai 1.8 seconds Proprietary. Commercial rights require active API usage contracts. Designed for instantaneous inference bursts. Both Replicate and fal.ai achieve identical 1.8-second median times on this model. Krea 2 Turbo Krea 2.0 seconds Open Weights / Proprietary Hybrid. Available via platform trial or API. Maintains the base model's compatibility with style references and LoRAs while utilizing Trajectory Distribution Matching (TDM) to accelerate the creative ideation loop. Midjourney v8.1 (Turbo Mode) Midjourney 3 – 6 seconds Proprietary. Commercial use requires an active Standard, Pro, or Mega tier subscription. Delivers generation speeds "three times faster than v8" while maintaining the model's signature "painterly realism with sophisticated lighting," though it requires a "higher credit cost". FLUX.2 [klein] 4B Black Forest Labs 3.9 seconds Open Weights. Permissive commercial use. The lightweight 4-billion parameter variant of the FLUX.2 architecture, balancing prompt adherence with high-speed generation. FLUX.2 [klein] 9B Black Forest Labs 4.6 seconds Open Weights. Permissive commercial use. The medium-weight 9-billion parameter open model. It scales up compositional intelligence while keeping generation firmly under the 5-second barrier. MAI Image 2 Efficient Microsoft 4 – 7 seconds Proprietary. Commercial use requires consumption-based API billing via Azure AI Foundry. A throughput-optimized variant explicitly designed to "out-pace Google’s Imagen Flash". It makes a slight trade-off in detail for "substantially lower latency" that suits "automated pipelines" perfectly. Midjourney v8.1 (Fast Mode) Midjourney 5 – 9 seconds Proprietary. Commercial use requires an active Standard, Pro, or Mega tier subscription. The standard operational mode for v8.1. Average wait times "consistently lands below 10 seconds for most prompts" while offering "excellent handling of complex multi-element scenes". FLUX.2 [dev] fal.ai / DeepInfra 6.1 – 6.4 seconds Open Weights (Non-Commercial). Strictly for research and non-commercial development. The developer-focused research model. API endpoint optimizations cause slight variance, with fal.ai operating at 6.1 seconds and DeepInfra at 6.4 seconds. Midjourney v8.1 (Relax Mode) Midjourney 8 – 14 seconds Proprietary. Commercial use requires an active Standard, Pro, or Mega tier subscription. Processes standard 1024x1024 resolution images without consuming fast GPU hours. The model retains "strong compositional instincts" and "consistent color grading and mood". FLUX.2 [pro] Black Forest Labs 11.1 seconds Proprietary. Commercial rights require paid API consumption. The closed, professional-grade tier. It drops extreme step-distillation to prioritize high-fidelity commercial rendering and strict spatial alignments. Seedream 4.0 BytePlus 11.6 seconds Proprietary. Commercial use via BytePlus enterprise contracts. The base commercial generation model for the Seedream architecture, focused on reliable, standard-resolution outputs. MAI Image 2 Standard Microsoft 12 – 20 seconds Proprietary. Commercial use requires consumption-based API billing via Azure AI Foundry. Operates as a "full-quality output optimized for photorealism". It acts as a literal renderer, delivering "high-fidelity skin tones and material textures" and "strong literal prompt adherence". Nano Banana Pro (Gemini 3 Pro Image) Google DeepMind 17.7 seconds Proprietary. Commercial rights granted via Gemini API terms. Prioritizes exact semantic accuracy and prompt adherence through an extended reasoning phase, trading raw speed for complex contextual execution. Seedream 4.5 BytePlus 18.2 seconds Proprietary. Commercial use via BytePlus enterprise contracts. The upgraded high-fidelity variant, requiring an additional 6.6 seconds of compute time over the 4.0 version to refine complex textures and text rendering. Krea 2 Large Krea 23.7 seconds Proprietary / Open Weights. Commercial rights depend on deployment. The un-distilled foundation model. It ignores the speed-focused Trajectory Distribution Matching of the Turbo variant to maximize aesthetic polish and structural stability. FLUX.2 [max] Black Forest Labs 25.6 seconds Proprietary. Closed enterprise API. The heaviest parameter model in the FLUX lineup. It operates exclusively as a deep reasoning renderer for complex commercial assets. GPT-Image-2 OpenAI 200.8 seconds Proprietary. Full commercial usage under standard OpenAI terms. A massive outlier in the latency landscape. It dedicates over three minutes to complex, multi-step semantic reasoning, likely utilizing an expansive chain-of-thought process prior to finalizing pixel outputs. Sources: Artificial Analysis, Krea, MindStudio.AI Architectural bifurcation and the 12B parameter Transformer At the technical core of the release sits an architectural framework built entirely from scratch: a Diffusion Transformer scaled to 12 billion parameters. Rather than deploying a single, heavily fine-tuned model for all downstream tasks, Krea open-sources two highly differentiated checkpoints captured at distinct milestones of the model's training lifecycle. Departing from multi-stream configurations for structural clarity, the core engine standardizes on a single-stream transformer block architecture wherein attention and MLP layers are shared natively between text and image tokens. To maximize computational efficiency, Krea incorporates a SwiGLU MLP layer operating at a 4x expansion factor alongside Grouped-Query Attention (GQA) combined with gated sigmoid attention layers to stabilize training dynamics. Timestep conditioning is heavily optimized; the network replaces traditional per-block MLP modules with a lightweight, per-block tunable bias term, successfully cutting total block modulation parameters by 20% to 30% and reallocating that parameter budget directly into core layers. Positional encoding is managed via a 3D Axial Rotary Position Embedding (RoPE) scheme mapping across individual frame, height, and width coordinate Krea 2 Raw represents an undistilled base release checkpoint taken directly from the mid-training stage of the larger Krea 2 Medium development cycle. Because it lacks post-training alignment, reinforcement learning from human feedback (RLHF), or final aesthetic distillation, Krea 2 Raw functions as a blank canvas. It retains a vast, uncurated latent space that makes it poorly suited for immediate out-of-the-box prompting, but highly optimized for structural training. Operating this model via the Hugging Face `diffusers` library requires a heavy compute footprint, executing via `Krea2Pipeline` in `torch.bfloat16` precision across 52 inference steps with a guidance scale of 3.5. To accelerate early-stage architectural convergence during the first epoch of this 256px baseline training phase, Krea applied internal Representation Alignment (iREPA) techniques before decoupling them to let the underlying model develop independent structural representations. The second checkpoint, Krea 2 Turbo, represents the opposite end of the optimization spectrum. It is a distilled, post-trained variant derived from Krea 2 Medium. Through knowledge distillation, the network's complex multi-step generation sequence is compressed into an incredibly lean operational profile. Krea 2 Turbo slashes the required generation cycle down to just 8 inference steps with a guidance scale of 0.0, enabling it to render native 2k resolution imagery on standard consumer-grade hardware in approximately 2 seconds. The underlying latent representations for both models are optimized through the integration of the Qwen Image VAE and the FLUX 2 VAE to guarantee rapid convergence while maintaining high reconstruction fidelity. Data and training The underlying dataset strategy for the Krea 2 family relies on a hybrid blend of publicly harvested data, third-party licensed image repositories, and highly curated synthetic datasets built via proprietary generation methods. Prior to final training, Krea processed these collections through rigorous algorithmic filters designed to strip out duplicative frames, low-resolution media, and explicit or harmful material, ensuring high fidelity and strong prompt compliance across both models. Krea enforces a zero-synthetic data policy within its primary pretraining mix. To prevent the upper-bound quality limitations and output biases induced by AI-generated data, the engineering team deployed custom in-house filtering classifiers built on top of DINOv3 and SigLIP-2 architectures to completely purge synthetic images at scale. Furthermore, rather than using traditional model-based aesthetic filters that inadvertently strip away artistic intents like motion blur, Krea preserves wide stylistic boundaries. The team trained a Sparse Autoencoder (SAE) on SigLIP-2 embeddings to isolate and filter out genuine visual artifacts using an unsupervised tagging framework. Krea 2 Raw vs. Krea 2 Turbo: Distinctions and use cases The release establishes a highly deliberate operational paradigm for professional studios and independent creators: "train on Raw, generate with Turbo." This workflow leverages the unique architectural properties of both open-weight files to optimize both training accuracy and rendering speed. In creative production pipelines, engineers can use Krea 2 Raw to train custom Low-Rank Adaptations (LoRAs) or domain-specific fine-tunes. Because the Raw checkpoint contains no baked-in stylistic opinions or aggressive post-training constraints, it absorbs unique aesthetic directions—such as architectural drafting styles, specific brand assets, or complex lighting designs—with high fidelity and zero stylistic interference. Once the training phase is complete, creators can port those exact LoRAs directly over to Krea 2 Turbo. This methodology is reflected in Krea's own development ecosystem, which hosts an in-house collection of custom LoRAs trained entirely on the Raw foundation model but optimized for execution within Turbo workflows. On the user-facing application layer, Krea integrates this dual-engine setup with a powerful style transfer system. Rather than relying on erratic text descriptions to achieve an artistic look, users can feed multiple style reference images directly into the system. Krea 2 maps these references across its latent space, allowing creators to isolate individual aesthetic components, combine distinct moodboards, adjust style strength via generative sliders, and fine-tune batch variation levels to maintain visual cohesion across large-scale design iterations. To address the gap between raw textual training captions and brief user inputs, Krea paired this suite with an advanced LLM Prompt Expander. Refined via Generalized Deep Q-Network Preference Optimization (GDPO) and trained on synthetic thinking traces to preserve intent reconstruction, the expander applies a photographic-medium bias to photorealistic requests and integrates an active DINOv3 embedding diversity score across rollout groups to prevent automated prompting routines from collapsing into a singular house style. While Krea 2 Medium and Krea 2 Large remain the company's flagship models for high-fidelity composition and absolute stylistic adherence, Turbo fills the critical role of rapid visual ideation. It serves as an interactive scratchpad for early concept creation, quick prompt experimentation, and iterative art direction where near-instantaneous feedback loops are required to maintain creative momentum. The custom license and its particulars The open-weight assets deploy under the Krea 2 Community License Agreement operating alongside an official Acceptable Use Policy. At a macro level, this legal framework mirrors recent industry trends toward commercial-use permissions that target small businesses while restricting large enterprise exploitation. The license explicitly permits individuals, independent creators, and small commercial companies to build applications, monetize generated imagery, and integrate the open weights directly into commercial software products without royalty obligations. Furthermore, Krea states that it "does not claim copyright or other intellectual property rights over content generated by users of this model," leaving output ownership entirely in the hands of the operator. For organizations scaling beyond this baseline, the ecosystem shifts into a paid, custom-tier structure. While Krea's official documentation lacks a rigid revenue threshold defining a "large enterprise," the company structurally demarcates the boundary based on organizational footprint: standard commercial usage caps at a "Business" tier accommodating up to 50 seats. Therefore, any entity requiring more than 50 seats, Single Sign-On (SSO) integrations, guaranteed Service Level Agreements (SLAs), or custom Data Processing Agreements (DPAs) qualifies as an Enterprise. These larger entities fall outside the free Community License scope and must pay for a custom commercial license—operating under "Custom Terms of Service"—negotiated directly with Krea's sales team. Additionally, developer access to Krea's official API remains entirely decoupled from the open-weights release; API usage operates as a distinct, paid service billed dynamically on a per-generation basis (measured in microdollars) and requires a prepaid USD balance independent of standard monthly compute subscriptions. However, a close examination reveals a significant structural shift regarding legal and behavioral compliance for all self-hosted deployments. Unlike traditional open-source permissions like the MIT or Apache 2.0 licenses—which grant unconditional usage rights and completely waive liability—the Krea 2 Community License implements strict downstream behavioral guardrails. Because Krea relinquishes centralized control over the downstream deployment of its open weights, the contract legally binds deployers to enforce content moderation protocols at the infrastructure layer. Under the terms of the agreement, any developer or platform hosting Krea 2 models must implement active input/output classifiers or equivalent content filtering mechanisms to actively prevent the generation of illegal materials, non-consensual intimate imagery (NCII), child sexual abuse material (CSAM), or defamatory assets. Developers who fail to deploy these defensive safety layers stand in immediate breach of contract, giving Krea the explicit right to update model weights or revoke access to the model family entirely. Background on Krea Founded in 2022 by audiovisual systems engineering dropouts Víctor Perez and Diego Rodriguez Prado, San Francisco-based Krea initially captured market traction as a highly fluid user interface layer built to orchestrate disparate, third-party AI generative engines. The startup's rapid scaling via product-led adoption culminated in an aggregate $83 million in disclosed venture capital funding from major VCs including Andreessen Horowitz and Bain Capital Ventures, as well as early-stage institutional backers including Pebblebed, Abstract Ventures, and Gradient Ventures. The company's user base surpassed 30 million individuals across 191 countries as of June 2026, according to its website. The open-weights launch of the Krea 2 model family represents the culmination of Krea’s deliberate evolution from a multi-model SaaS aggregator into a self-sustaining media research lab. Early in its lifecycle, Krea focused on building workflow tools, editing systems, and a node-based automation pipeline that allowed digital artists to unify models from competitors like Runway, Midjourney, and Adobe under a single subscription. However, to insulate itself against upstream platform dependencies and supplier margin pressures, the company aggressively shifted toward developing proprietary architectures. This transition began taking public shape in July 2025 with the open-weights release of the custom-curated FLUX.1 Krea checkpoint, followed in October 2025 by Krea Realtime 14B—an autoregressive video model distilled from Wan 2.1 capable of rendering 11 frames per second on localized enterprise hardware. This underlying technical maturation parallels Krea's accelerating push into high-end enterprise workflows. Large-scale creative production operations have shifted toward treating Krea as core creative infrastructure; for example, the digital creative services platform Superside reported migrating workflows from fragmented open-source setups to route roughly 80 percent of its total AI generative production through Krea. Furthermore, Krea established a strategic co-development partnership with Copenhagen-headquartered architecture firm Henning Larsen to build highly restricted, domain-specific design tools tuned to meet the compliance frameworks mandated by the EU AI Act. By releasing Krea 2 Raw and Turbo as open weights, Krea is continuing its expansion from an AI tools provider to being a model provider in its own right. An alternative to typical rigid AI imagery APIs? Creators are focusing heavily on the structural freedom offered by the unaligned Raw checkpoint, viewing it as an important alternative to the locked-down APIs provided by closed-source models. Through the official announcement on X, Krea emphasized the foundational shift this launch represents for open AI workflows. Developers note that by treating AI as an "actual creative medium" that feels "raw, flexible, unopinionated, and unconstrained," Krea is intentionally providing an infrastructure that creators can "break if [they] want to," moving far away from the rigid safety guardrails that frequently limit the visual range of competing enterprise tools. As independent model builders begin compiling the Hugging Face repositories, the practical value of the release will be determined by how effectively the open-source community can scale customized LoRAs using Krea 2 Raw. By providing clear commercial terms and lowering hardware entry barriers via Turbo's 8-step inference pipeline, Krea has introduced a highly competitive alternative to the open-weights market, challenging dominant models by prioritizing artistic control over centralized corporate alignment.
Masayoshi Son Calls AI Bubble Talk an ‘Insult,’ Delays Retiring
SoftBank Group Corp.’s Masayoshi Son brushed aside concerns about an AI bubble, saying that such a characterization is inappropriate for a revolution that’s only just begun. He vowed to remain at the helm of his investment group much longer to capitalize on the AI boom.
ByteDance Seeks $20 Billion in Its Largest-Ever Global Loan
ByteDance Ltd., the developer of TikTok, is in preliminary talks with banks for a borrowing of about $20 billion, people familiar with the matter said, in what would be the firm’s largest offshore loan yet at a time when it’s boosting investments in artificial intelligence.
The Three AImigos versus The Magnificent Seven
Attention Markets Hypothesis > Efficient Markets Hypothesis
Leveraged Korea ETFs Sold Estimated $6 Billion of Shares in Rout
Leveraged exchange-traded funds tracking Samsung Electronics Co. or SK Hynix Inc. probably sold a combined $6 billion of the Korean chipmakers’ shares Tuesday to maintain their ratios, underscoring such products are amplifying market moves, Bloomberg Intelligence says.
Global M&A industry trends: 2026 mid-year outlook | PwC
Supersizing M&A for the AI era. Fewer, much larger deals set the trend as AI rips up the old playbook and ushers in a disruptive new age for dealmakers.
SpaceX Sells $25 Billion of Bonds, Cuts Interest Costs
SpaceX sold $25 billion of investment-grade bonds on Tuesday, marking the final step to replace the costly debt that had helped finance Elon Musk’s acquisition of X, then known as Twitter, as well as the expensive loans and bonds issued by xAI to bridge its rapid cash drain. Bloomberg's Manuel Baigorri reports. (Source: Bloomberg)
Asia Tech Shares Swing Wildly as A.I. Jitters Persist
The turbulence in Asia’s chip-dominated stock markets highlighted how heavily global equities have come to depend on enthusiasm for artificial intelligence.
The global tech sell-off is a reckoning with AI valuations that were always going to come - Startup Fortune
A two-day rout in global technology stocks has wiped hundreds of billions from the sector, with SpaceX shedding $900 billion from its peak, Samsung and SK
AlpSemi raises €17m for development of wide-bandgap power switches for AI data centers
French startup AlpSemi has raised €17 million ($19.5m) to support the development of its next-generation power switches. The round was led by Yotta Capital and saw participation from SE Ventures, Navitas Semiconductor, and Cycle Group. – AlpSemi Founded in 2024 by the company’s respective CEO and CTO, Frédéric Dupont and Fabrice Letertre, Grenoble-based AlpSemi develops […]
Is there an AI bubble? VCs on valuations and ARR inflation | TechCrunch
This episode features a conversation recorded live at StrictlyVC LA in El Segundo between Connie Loizos; Chang Xu, partner at Basis Set Ventures; and Carter Reum, founder of M13. Together, they discuss whether today’s AI boom represents a bubble, how investors are thinking about soaring startup ...
The 5 Types of AI Investment–and How to Capture Their Value
There are five types of AI investments—two tactical to maintain market position and three strategic to build durable advantage—and none can be measured against traditional ROI calculation tools. Tactical investments include ones made for competitive parity and option value, while strategic ...
r/wallstreetbets on Reddit: Cerebras reports 92% revenue growth in chipmaker's first earnings report since IPO
They make wafer-scale AI chips.
Is AI 'one big bubble'? Behind the tech sell-off : NPR
Investors are selling off AI-related stocks as doubts are starting to surface over whether the massive spending on AI is worth the investment and whether it's "one big bubble."
AI IPO Boom 2026: SpaceX, OpenAI & Anthropic Investment Guide
The 2026 IPO landscape is dominated by three extraordinary companies that have achieved valuations previously reserved for the largest public technology giants. OpenAI, the creator of ChatGPT, has confidentially filed its S-1 registration statement and is valued at approximately $920 billion following its most recent financing round. Anthropic, developer of the Claude family of AI models, filed its draft S-1 on June ...
AI rally hits a wall: South Korea’s Kospi plunges 10%, triggers trading halt
South Korea's Kospi plunged nearly 10 per cent on Tuesday, triggering a trading halt as investors dumped AI-linked chip stocks such as Samsung Electronics and SK Hynix, raising concerns that the global artificial intelligence rally may be overheating.
Microsoft’s $37 Billion AI Run Rate Points to 33% Upside Potential - 24/7 Wall St.
Microsoft (NASDAQ:MSFT | MSFT Price Prediction) is the rare mega-cap where the bear case centers on valuation rather than business quality: the stock got ahead of itself. After a sharp drawdown from $551.05 in 2025 to today’s level, the math has reset. Our model says the setup now favors ...
K-Drama on Tech Street: AI rout hits global markets - The Economic Times
Technology stocks experienced a significant downturn, pulling major indices lower as a sharp selloff in Korean chipmakers raised concerns about the sustainability of the AI-driven market surge. Nvidia and Micron were among the biggest decliners. This dip, triggered by reports of SK Hynix slowing ...
US Stock Market Today: Live Updates 23.06.2026
Baby Bunting Group lowered second-half ... IFM Investors increased control above 50% in takeover bid. ... June 24, 2026, 2:42 AM EDT. Jeremy Grantham, renowned for forecasting stock market bubbles, warns of a potential bubble in AI stocks. He likens today’s AI valuations, including ...
AI earnings momentum still intact, Citi raises S&P 500 target to 8,100 - CNBC TV18
Drew Pettit, Director-US Equity Strategy/ETF Analysis & Strategy Research at Citi expects Brent crude prices to trend lower, while cautioning that a more hawkish Federal Reserve could offset some of the benefits. He also argues that today's market exuberance, though elevated, is still far from ...
Infosys Slips to Rs. 1,034 as Global Tech Rout Hits IT Stocks
Infosys, TCS, Wipro, and HCLTech declined amid a global tech sell-off. Analysts cite weak demand, AI uncertainty, and cautious spending outlook.
Wall Street ends lower on semiconductor selloff amid AI spending concerns
The Nasdaq fell 2.2% and the S&P 500 dropped 1.4% as semiconductor stocks sold off amid growing concerns about hyperscaler AI spending sustainability.
Bain Capital's Zhu on Investing in China
Jonathan Zhu, Greater China Chairman at Bain Capital, discusses his outlook and strategies for investing in the Chinese market. He speaks with Stephen Engle on the sidelines of the World Economic Forum in Dalian on "Bloomberg: The Asia Trade". (Source: Bloomberg)
US Market Crash: Nasdaq 100 Drops 1000 Points Amid Tech Sell-Off Ahead of Micron Results - Goodreturns
The Philadelphia Semiconductor Index fell nearly 8%, with all 30 constituents closing lower. The move was significant because the index tracks several companies central to the AI hardware supply chain.
AI Chipmakers Dominate Emerging-Market Indices, Raising Risk | Let's Data Science
Editorial analysis: Concentration of index weight in a few semiconductor names increases vulnerability to semiconductor-cycle volatility and supply-chain shocks, which is relevant for portfolio construction and risk monitoring for practitioners tracking the AI hardware supply chain.
How to measure the economy-wide impact of AI | International Labour Organization
Poland becomes the first country application of a new World Bank Group–ILO technical collaboration designed to assess the potential macroeconomic impacts of Artificial Intelligence.
How to Survive the AI Shock | Foreign Affairs
According to the Financial Times, ... on AI in 2026 to $725 billion, a 77 percent increase from the roughly $410 billion they collectively spent last year. The other means of increasing productivity, innovation, involves discovering new goods and services and more efficient ways to produce existing ones. Every day, companies are using AI to discover and take advantage of efficiencies and automate routine ...
Inside Crypter-as-a-Service: An Ecosystem Analysis of the exploit.in Underground Forum Research Talks
arXiv:2606.24226v1 Announce Type: new Abstract: Crypter-as-a-Service (CraaS) has become a key enabling layer of the contemporary malware economy by providing on-demand evasion capabilities through underground service markets. In this paper, we present a longitudinal characterization of the CraaS ecosystem on exploit.in, a major Russian-language cybercrime forum with a presence on both the clear web and the dark web. From a collection of approximately 1,000,000 posts, we combine keyword filtering, LLM-assisted annotation, and manual validation to extract a corpus of 491 threads and 2,949 posts spanning January 2020 to August 2025. Our analysis shows that crypters on exploit.in are not merely sold as static tools, but as continuously maintained operational services whose value depends on recurring stub renewal - sometimes on a daily basis - sustained antivirus evasion, and trust-based delivery. We develop a taxonomy of five seller types and four buyer profiles, and map the buyer-seller correspondences that structure market transactions. We further document pricing models ranging from low-cost per-build Telegram bot services to high-end custom development and salaried recruitment. Using social-network analysis, we find that the market is hierarchically structured around a small core of highly central actors, many of whom appear to function as trust brokers or other influential intermediaries, while its stability relies on a broader trust and governance infrastructure including escrow, guarantors, reputation systems, and security deposits. Finally, we discuss differences between the CraaS model observed on exploit.in and that reported on HackForums. Although both forums share similar service logics, our corpus suggests that exploit.in exhibits a more professionalized and service-oriented CraaS configuration.
Breaking the Filter Bubble: A Semantic Pareto-DQN Framework for Multi-Objective Recommendation
arXiv:2606.24042v1 Announce Type: new Abstract: Recommender systems often induce filter bubbles and semantic homogenization by monolithically optimizing for immediate user engagement. Standard single-objective models, including traditional Deep Q-Networks, are ill-equipped to navigate the trade-offs between platform retention and critical societal values like information diversity and provider fairness. To address these limitations, we introduce a multi-objective reinforcement learning framework that formalizes recommendation as a semantic multi-objective Markov decision process. By integrating high-fidelity semantic embeddings with a Pareto-DQN agent, our architecture treats engagement, diversity, and fairness as distinct, non-aggregable reward signals, avoiding the pitfalls of static reward scalarization. Empirical evaluations on the MovieLens small dataset shows that our hypervolume based action selection disrupts the feedback loops responsible for semantic collapse. By sustaining high state-trajectory variance, the Pareto-DQN effectively maps the Pareto frontier, achieving gains in auxiliary societal objectives with only marginal impacts on engagement. This work provides a path toward intrinsically aligned, responsible recommender systems.
US remains global leader in AI, but China rapidly closing gap with cheaper models: JP Morgan - The Economic Times
The United States stands at the forefront of AI development, yet China is swiftly closing the gap with cost-efficient AI systems. Chinese companies are making headway in corporate environments by offering significantly lower operational costs than their American counterparts.
MediaTek-Global Unichip tie-up talk puts TSMC's AI ASIC ecosystem on watch
Cloud service providers are accelerating in-house AI chip development, lifting demand for application-specific integrated circuits, or ASICs, and reshaping collaboration across the semiconductor supply chain.
Marathon, BP, other gas stations accused of AI-powered scheme to hike gas prices in US
A lawsuit filed Monday accuses major gas station chains of using an AI-powered tool to artificially inflate gasoline prices in California to increase profit margins.
AI lab musical chairs hits Google the hardest
Google DeepMind has lost several high-profile researchers as competition for top talent intensifies among major AI labs chasing artificial general intelligence.
US patent headwinds driving trade secret shift, foreign enforcement
A new report from Cornerstone Research suggests that two decades of patent reform are driving innovators to shift toward trade secrets and foreign enforcement of their rights.
Modeling User Redemption Behavior in Complex Incentive Digital Environment: An Empirical Study Using Large-Scale Transactional Data
arXiv:2509.14508v2 Announce Type: replace Abstract: The digital economy implements complex incentive systems to retain users through point redemption. Understanding user behavior in such complex incentive structures presents a fundamental challenge, especially in estimating the value of these digital assets against traditional money. This study tackles this question by analyzing large-scale, real-world transaction data from a popular personal finance application that captures both monetary spending and point-based transactions. We find that point usage is linked to demographics. Our analysis using a natural experiment and a causal inference technique reveals that a large point grant stimulated an increase in point spending without a detectable effect on cash expenditure. We then find an association between consumers' shopping styles and their point redemption patterns. This study, on a massive real-world economic ecosystem, examines how consumers behave in multi-currency environments, with direct implications for modeling economic behavior and designing digital platforms.
Machine Learning Classification and Portfolio Construction: Does the Loss Function Matter?
arXiv:2108.02283v3 Announce Type: replace-cross Abstract: Classification outperforms regression across matched machine learning models in portfolio construction. A stacking ensemble of gradient boosted tree, random forest, and neural network yields a value-weighted annualized Sharpe ratio of 1.83 for classification and 1.11 for regression. This outperformance persists in multiclass settings, across subsamples, and after transaction costs. Spanning tests show that classification retains economically large alphas after we control for regression, whereas regression alphas shrink substantially once we control for classification. These results indicate that classification extracts more return information than matched regression. Our diagnostics trace classification's advantage to sharper and more precise separation of return deciles.
Zhipu Said to Weigh Multibillion-Dollar Share Sale in Hong Kong After 2,000% Gain
Chinese artificial-intelligence model maker Zhipu is considering a share sale to raise several billion US dollars in Hong Kong, people familiar with the matter said, after soaring 2,000% since its listing in January.
An AI venture soared as it touted links to Trump. Now it’s fighting for survival
Shares in Fermi collapsed after it failed to secure an anchor tenant and its CEO was fired. Can it ever fulfil its promise?
Drone start-up Stark wins more funding from Peter Thiel-backed firm
US billionaire’s fund participated in €500mn funding round despite previous criticism of company’s links with financier
Venture Capital & Startup Funding Roundup, June 23, 2026 - Tech Startups
It’s Tuesday, June 23, 2026, and the last 12 hours of startup financing did not look like a broad market rebound. They looked like a market making a narrower, harder-edged bet. Capital went to software that sits inside real operating systems, to infrastructure that decides who gets served, ...
Paris-based AI company Tsuga raises $35m in Series A funding
The raise will be put towards scaling Tsuga GTM motion and accelerating the rollout of the platform’s AI agents. Read more: Paris-based AI company Tsuga raises $35m in Series A funding
Menlo Ventures' Ganesan on AI Bets and Startup Success | StartupHub.ai
Menlo Ventures has raised a $3 billion fund to back AI startups, signaling a strong belief in the technology's transformative potential and a strategic focus on
Irish space-tech Ubotica raises $11m to scale AI marine monitoring
Ubotica has seen a ‘significant demand’ for its maritime intelligence platform over the past year, it said. Read more: Irish space-tech Ubotica raises $11m to scale AI marine monitoring
Labor, Society & Culture
21,000 Oracle jobs vanish amid Big Red's big bets on AI
Annual report reveals workforce fell from 162,000 to 141,000 in a year as company pours billions into datacenter expansion
Indian Tech’s Nifty Share Shrinks to Record Low on AI Worries
India’s software exporters are steadily losing their sway on the country’s stock market as concerns over artificial intelligence-led disruption trigger a prolonged selloff in the sector.
Exploring the societal impacts of AI
During the AI and Society Forum, leading MIT researchers examined critical questions about AI’s influence on employment and democracy.
Anthropic engineering head says Claude Code made employees’ work a ‘lonely experience’—and it could hint at Big Tech’s bigger morale problem
Tech workers are some of AI’s biggest users and supporters, but the people using the tech the most may also be experiencing the most anxiety about it.
WSJ Intelligence Study: Uniquely Human Skills Deemed "Non-Replicable" in Automated Future
Philip Morris International (PMI) (NYSE: PM) and WSJ Intelligence, the in-house thought leadership consultancy for The Wall Street Journal's commercial sales...
The Generative AI Transformation: Evidence-Based Insights on Labor Market Disruption and Organizational Adaptation
The emergence of generative artificial intelligence has triggered unprecedented debate about workforce displacement and labor market transformation. Recent empirical evidence reveals a more nuanced reality than simple replacement narratives suggest. Following ChatGPT's public launch in November ...
Skilled Workers Are Finally Gaining Ground. AI Will Decide Whether They Keep It.
/PRNewswire/ -- A new report from Opportunity@Work argues that the country's future competitiveness depends on a question being decided right now, largely out...
How AI Is Quietly Redefining What Good Performance Looks Like At Work
With AI automating routine tasks, employees face heightened expectations for faster, higher-quality output.
AI Layoffs Are Backfiring — and the Data Already Proves It | by Noor Mohamad | Jun, 2026 | Medium
Every few weeks another executive steps onto a stage or a podcast and says some version of the same thing: AI is doing the work now, so we don’t need as many people.
Social dialogue and collective bargaining: the key to fair generative AI transformations
New report looks at the impact of Generative AI on ICT, Finance and Audiovisual sectors, highlighting the need for human oversight, regulation and collective bargaining.
Apple Just Closed Its First Unionized U.S. Store
The company said it had shuttered a Maryland location because of “declining conditions” in a surrounding mall. The union accuses Apple of retaliation.
US Supreme Court limits scope of foreign human rights claims
Justices refuse to consider complaint alleging Cisco enabled Chinese surveillance of banned religious group
AI companies should release environmental impact, commit to clean energy, says UN chief | Business | dailygazette.com
United Nations Secretary-General António Guterres is calling on artificial intelligence companies to release information about the carbon, water and land used to power their systems. Guterres spoke Tuesday during an
It's Safer to Give Personhood to Bears than to Artificial Intelligence
arXiv:2606.12440v3 Announce Type: replace Abstract: Artificial intelligence (AI) developers are rhetorically flirting with the idea that AI systems might have interests or moral rights. While there has been a large volume of research on whether AI deserves rights, there has been less exploration of what AI rights would mean in practice. This paper explores the institutional dimension of AI rights: what it would take to recognize moral or legal rights for AIs, and the attendant opportunities and dangers. Unlike all other nonhuman entities to which humanity has extended rights, AI systems are in principle capable of acquiring and wielding institutional power without human aid and mediation. AIs with rights would be able to legitimately, and AIs with power able to unpreventably, abridge human interests. Accordingly, giving rights even to rather dumb AI systems would entail binding the fate of humanity to potentially unpredictable nonhumans. Accordingly, I defend the rather grandiose claim that to empower AI to claim or to exercise inherent rights would be a world-historical gamble with human self-determination, which no individual researcher, firm, state, or even international organization has the moral right to authorize.
Affective AI Safety: The Missing Piece in LLM Safety
arXiv:2606.23380v2 Announce Type: replace Abstract: AI safety research has focused predominantly on epistemic and physical harms (e.g., misinformation, bias, system reliability) while the risks that arise from AI systems' engagement with human emotional life have remained fragmented and undertheorised. We propose affective safety as a unified class of AI safety concerns grounded in the fact that humans are affective beings. We develop a taxonomy of affective harms and identify recurring harm types: (1) affective self-alienation, (2) fairness and bias harms, and (3) relational harms. We show that their recurrence across system types reflects structural properties of how AI systems engage with human emotion and survey the current safety landscape and show that existing frameworks address affective safety either narrowly or not at all. We conclude by identifying the technical and regulatory challenges specific to this class of harms and argue that affective safety requires dedicated frameworks that engage with cumulative, relational, and identity-level effects.
Tesla Crash That Killed a Texas Woman Will be Investigated by Federal Regulators
The car’s driver-assistance system was in use when the crash killed a woman on Friday, the police said.
Meta halts worker tracking for AI training due to privacy fears
The company had started just two months ago tracking workers’ computer usage for AI training data.
Societal Alignment Frameworks Can Improve LLM Alignment
arXiv:2503.00069v2 Announce Type: replace Abstract: Recent progress in large language models (LLMs) has focused on producing responses that meet human expectations and align with shared values - a process coined alignment. However, aligning LLMs remains challenging due to the inherent disconnect between the complexity of human values and the narrow nature of the technological approaches designed to address them. Current alignment methods often lead to misspecified objectives, reflecting the broader issue of incomplete contracts, the impracticality of specifying a contract between a model developer, and the model that accounts for every scenario in LLM alignment. In this paper, we argue that improving LLM alignment requires incorporating insights from societal alignment frameworks, including social, economic, and contractual alignment, and discuss potential solutions drawn from these domains. Given the role of uncertainty within societal alignment frameworks, we then investigate how it manifests in LLM alignment. We end our discussion by offering an alternative view on LLM alignment, framing the underspecified nature of its objectives as an opportunity rather than perfect their specification. Beyond technical improvements in LLM alignment, we discuss the need for participatory alignment interface designs.
No Certificate, No Categorical Speech Act: A Brouwerian Assertibility Constraint for Public Reason
arXiv:2603.03971v3 Announce Type: replace Abstract: Generative AI can convert uncertainty into authoritative-seeming verdicts, intensifying the hypersuasive force of automated speech and displacing the justificatory work on which democratic epistemic agency depends. As a corrective, I propose a Brouwer-inspired assertibility constraint for responsible AI: in high-stakes domains, systems may assert or deny claims only if they can provide a publicly inspectable and contestable certificate of entitlement; otherwise they must return Undetermined. This constraint yields a three-status interface semantics (Asserted, Denied, Undetermined) in which statuses mark entitlement to categorical speech rather than truth values of the underlying world-claim. The semantics cleanly separates internal entitlement from public standing while connecting them via the certificate as a boundary object. It also produces a time-indexed entitlement profile that is stable under numerical refinement yet revisable as the public record changes. I operationalize the constraint through decision-layer gating of threshold and argmax decisions, using internal witnesses (e.g., sound bounds or separation margins where available, and contestable surrogates otherwise) and an output contract with reason-coded abstentions. A design lemma shows that any total, certificate-sound binary interface yields witnessed decidability of the deployed predicate on its declared scope, so Undetermined is not a tunable reject option but a mandatory status whenever no adequate forcing witness is available. By making outputs answerable to challengeable warrants rather than confidence alone, the paper aims to preserve epistemic agency against the persuasive pull of automated speech in public justification.
Critique of Agent Model
arXiv:2606.23991v1 Announce Type: new Abstract: What is an agent? What constitutes agency? With the rise of Large Language Model (LLM) systems marketed as ``coding agents'', ``AI co-scientists'', and other ``agentic" tools that promise to drive up productivity, and at the same time, ``existential" concerns such as AI escaping human control with destructive power under a speculative ``machine agency" against humans, it has become essential to clarify where automation ends and agency begins, both for building capable systems and for understanding whether and what to fear. Drawing on Descartes' grounding of agency in independent thought, and on portrayals of autonomous beings in science fiction, we survey the current landscape of AI agents, and analyze agent architectures along five dimensions: goal, identity, decision-making, self-regulation, and learning. Specifically, we argue that genuine agency requires these structures to be \emph{internalized within the system itself} rather than assembled through external scaffolding. This distinction between \emph{agentic} systems, whose competence resides in engineered workflows, and \emph{agentive} systems, whose capabilities (including social interaction) arise endogenously, defines the boundary between systems designed for prescribed tasks, and those capable of operating in the open world with true autonomy. Building on this analysis, we propose the Goal-Identity-Configurator (GIC) architecture for a general-purpose agent model, combining hierarchical goal decomposition, identity evolution, simulative reasoning grounded in a separately trained world model, learned self-regulation, and self-directed learning from both real and simulated experience. Furthermore, we share insight on the auditability, controllability, and safety of agentive systems that possess greater autonomy and ``agency", but remain under human oversight.
Safe and Generalizable Hierarchical Multi-Agent RL via Constraint Manifold Control
arXiv:2606.24010v1 Announce Type: new Abstract: Multi-agent systems are widely used in safety-critical applications that require coordinated behavior under strict safety constraints. Existing approaches face a fundamental trade-off: learning-based methods achieve strong empirical performance but lack theoretical safety guarantees, while control-theoretic methods enforce safety but often lead to overly conservative and inefficient behaviors. We propose a hierarchical multi-agent reinforcement learning framework that enforces hard safety constraints under mild assumptions at low level via a constraint manifold, while enabling effective coordination through high-level policy learning. Our approach provides theoretical safety guarantees in the multi-agent setting and yields stationary learning dynamics, thereby enabling stable and efficient training. Empirically, our method achieves competitive performance while maintaining nearly perfect safety rates, and generalizes effectively to varying numbers of agents and obstacles.
AI in the classroom prompts tide of concern from US parents and experts
While tech companies and Trump have been pushing teachers to use AI in the classroom, many argue that there is little evidence that it would actually help children In October, Kelly Clancy’s son received an assignment in sixth grade at a middle school in Brooklyn, New York, to create a science experiment and then ask Google Gemini, an artificial intelligence chatbot, for feedback, she said. Clancy, who has three children in New York City public schools, told the teacher that the bot “is something that just teaches kids that they can have machines do the thinking for them”, instead of suggesting: “Let’s talk to your partners. What about the science experiment could you improve?” Continue reading...
Sharing a love for calculus
The national conversation about the value of education is currently dominated by speculation about the risks and positive potential of AI. Whatever your own perspective on that debate, I hope you’ll be glad to know that MIT is also working on a deeply important but comparatively old-fashioned challenge: American high school students’ startlingly uneven access…
Technology & Infrastructure
Tencent Testing New AI Agent for WeChat Workplace App
Tencent Holdings Ltd. is preparing to launch an AI agent for its Slack-like enterprise communication app, intensifying a high-stakes battle among Chinese tech giants to lock users into their ecosystems in the post-ChatGPT era.
Anthropic launches Claude Tag, replacing its Slack app with a persistent AI teammate that learns, monitors and works autonomously
Anthropic on Tuesday launched Claude Tag, a new product that embeds its most advanced AI model directly inside Slack as a persistent, shared teammate that anyone on a team can delegate work to by simply typing @Claude. The product, available today in beta for Claude Enterprise and Team customers, replaces Anthropic's existing Claude in Slack app and represents the company's most aggressive move yet to colonize the enterprise collaboration layer — the place where decisions get made, work gets assigned, and institutional knowledge accumulates in real time. For enterprise technology leaders who have spent the past two years evaluating where AI fits into their operational stack, Claude Tag reframes the question entirely. This is not a chatbot, a coding assistant, or a search tool bolted onto a messaging platform. It is an AI agent designed to function as a standing member of a team — one that builds memory, takes initiative, works asynchronously, and interacts with every person in a channel rather than serving a single user. The implications for enterprise workflow, governance, and vendor strategy are significant. Anthropic says 65% of its own product team's code is now created by its internal version of Claude Tag, and the company runs internal support and data insight channels through the same system. The claim is striking: Anthropic is asserting that the majority of its own product engineering output already flows through the tool it just put in customers' hands. How Claude Tag works inside enterprise Slack channels At its core, Claude Tag works like this: an administrator pairs it with a Slack workspace, grants it access to specific tools and data sources, sets spending limits, and defines which channels it can operate in. From that point on, any team member in those channels can tag @Claude with a request — write a pull request, pull sales numbers, run a data analysis — and Claude will break the task into stages, execute them using the tools it has access to, and respond in a Slack thread with the result. The product runs on Claude Opus 4.8, the model Anthropic released less than a month ago. Four capabilities differentiate Claude Tag from its predecessors and from competing integrations. First, it is multiplayer. Within a given Slack channel, there is one Claude that interacts with everyone, not a separate instance per user. Anyone can see what it is working on, and anyone can pick up the conversation where the last person left off. This is a direct contrast to most existing AI integrations in Slack, which tend to operate as single-player tools. Second, it learns over time. As Claude follows along with its channel, it accumulates context about the work happening there. Users do not need to re-explain projects from scratch. If granted permission, Claude can also pull context from other Slack channels and data sources, though Anthropic says it will not report from private channels. Third, it takes initiative. With ambient behavior enabled, Claude will proactively surface relevant information from across the channels it monitors and the tools it is connected to, and will follow up on threads or tasks that have gone quiet without resolution. This is a notable expansion of agency: Claude is not just responding to requests but monitoring the information environment and deciding what its human teammates need to know. Fourth, it works asynchronously, pursuing projects autonomously over hours or days. Anthropic says its own teams "now spend much more of our time delegating tasks to many Claudes in parallel." Enterprise security controls and administrative governance get a central role Anthropic has designed the system with enterprise-grade isolation at its center. System administrators define separate Claude identities for different uses, scoped to specific channels with specific tools and data access. Everything, including Claude's accumulated memories, stays within those boundaries. A Claude configured for sales work will not share memories or data access with one configured for engineering. Administrators can set token-spend limits at both the organizational and channel level, and can review a complete log of every action Claude has taken and which user requested each task. For organizations managing compliance, audit, or regulatory requirements, this logging and scoping architecture is table stakes — and its absence has been a dealbreaker for many enterprises evaluating AI collaboration tools over the past year. Migration from the existing Claude in Slack app requires an administrator opt-in within 30 days, and Anthropic says it is issuing introductory launch credits to eligible Enterprise and Team organizations. The four-step setup process — pair with Slack, connect tools, set spend limits, test in a private channel — is designed to reduce friction for IT teams already managing sprawling SaaS portfolios. The Slack battleground is now the most contested real estate in enterprise AI Claude Tag arrives in the middle of what has become the most fiercely contested territory in enterprise AI: the Slack channel. Slack itself has been aggressively positioning the platform as an "agentic operating system," and the major AI players have responded by racing to plant their flags. Salesforce, which acquired Slack for $27.7 billion in 2021, announced more than 30 new capabilities for Slackbot in March — the most sweeping overhaul of the platform since the acquisition — transforming it from a simple conversational assistant into a full-spectrum enterprise agent. OpenAI introduced "Workspace Agents" in April, allowing enterprise subscribers to design agents that take on work tasks across third-party apps including Slack, Google Drive, Microsoft apps, Salesforce, and Notion. Perplexity launched its enterprise "Computer" agent with direct Slack integration, letting employees query @computer directly inside Slack channels. Cognition's Devin, the autonomous AI software engineer, has been built around Slack as a primary interface since its early days. Even Microsoft has brought GitHub Copilot into Teams. The logic driving this convergence is straightforward: the average enterprise juggles over 1,000 applications, and employees waste countless hours on context switching, draining productivity by up to 40%. Whichever AI system becomes the default presence in the communication layer where work is coordinated gains an enormous distribution advantage — and, critically, an enormous data advantage. The AI that lives in the channel where work happens absorbs the institutional context that makes it increasingly difficult to replace. Anthropic built Claude Tag on a foundation two years in the making To understand Claude Tag's strategic significance, it helps to trace the product arc that led to it. Anthropic first integrated Claude with Slack in October 2025, offering two-way connectivity: users could invoke Claude from within Slack or connect Slack as a data source for Claude's chatbot. The initial integration was focused on individual productivity — direct messages, AI assistant panels, and thread participation. In January 2026, Anthropic expanded Claude's Slack presence when it launched interactive Claude apps, which included workplace tools like Slack, Canva, Figma, Box, and Clay. In parallel, Anthropic was building out its enterprise infrastructure stack. In August 2025, the company bundled Claude Code into enterprise plans, a move its product lead Scott White called "the most requested feature from our business team and enterprise customers." In April 2026, Anthropic launched Claude Managed Agents, a suite of composable APIs for building and deploying cloud-hosted AI agents at scale, with early adopters including Notion, Rakuten, Asana, and Sentry. Then came Claude Opus 4.8 in late May, which Anthropic described as "a more effective collaborator" with "sharper judgement, more honesty about its progress, and the ability to work independently for longer than its predecessors." Benchmark improvements included a jump in agentic coding scores from 64.3% to 69.2% and a knowledge work score increase from 1753 to 1890. Claude Tag is the synthesis of all of these threads — combining the Slack channel presence, the enterprise security architecture, the Managed Agents infrastructure, and the Opus 4.8 model's improved agentic capabilities into a single product that Anthropic frames as "the beginning of an evolution of Claude Code." Anthropic's explosive growth explains why it is betting big on the collaboration layer The financial stakes behind this launch are enormous. Anthropic raised $65 billion in Series H funding in late May at a $965 billion post-money valuation, and its run-rate revenue crossed $47 billion earlier this month. Claude Code's run-rate revenue alone has grown to over $2.5 billion, more than doubling since the beginning of 2026, and enterprise use has grown to represent over half of all Claude Code revenue. Those numbers explain why Anthropic is investing so heavily in channel-level presence. Every enterprise customer who grants Claude persistent access to a Slack channel — with connected tools, accumulated context, and ambient monitoring enabled — represents a dramatically deeper integration than a chatbot conversation or an API call. The usage patterns become stickier, the token consumption grows, and the switching costs rise. Deloitte's deployment of Claude across more than 470,000 employees in 150 countries — reportedly its largest-ever enterprise AI deployment — illustrates the scale at which these dynamics play out. The broader market trajectory reinforces the bet. Fortune Business Insights projects the global agentic AI market will grow from $9.14 billion in 2026 to $139 billion by 2034, and Gartner forecasts that 40% of enterprise applications will feature task-specific AI agents by 2026, up from less than 5% in 2025. Anthropic is not alone in seeing this future, but with Claude Tag it is making one of the most direct plays yet to own the enterprise agent layer. The risks enterprise buyers need to weigh before granting Claude a permanent seat at the table Claude Tag raises several questions that enterprise buyers will need to evaluate carefully. The first is vendor dependency. As VentureBeat reported when analyzing Claude Managed Agents earlier this year, once an organization's agents, operational configurations, and monitoring run on Anthropic's managed infrastructure, switching costs increase significantly. Claude Tag deepens this dynamic: a Claude that has accumulated months of channel context and institutional memory becomes very difficult to replace. Enterprise procurement teams accustomed to negotiating multi-cloud flexibility will need to think hard about what it means to give a single vendor's AI persistent access to the communication layer where institutional knowledge lives. The second is governance around ambient monitoring. The proactive behavior mode — in which Claude monitors channels and surfaces information it decides is relevant — represents a meaningful expansion of what enterprise AI systems do. Organizations will need to develop clear frameworks for an AI agent that is not just responding to requests but actively surveilling information flows and making editorial judgments about what humans need to know. For regulated industries, this raises questions that existing AI governance policies may not yet address. The third is pricing. Anthropic has not published detailed pricing for Claude Tag beyond noting that it runs on token-based spending with administrative controls. For an agent that monitors channels continuously, builds memory, and works asynchronously over hours or days, the token consumption profile could look very different from traditional AI usage. And the fourth is reliability: Anthropic has been candid in recent months about infrastructure strain caused by surging demand, and for a product positioned as an always-on team member, downtime carries a different kind of cost than it does for a tool invoked on demand. What Claude Tag signals about the future of enterprise work Anthropic says its goal is to expand Claude Tag beyond Slack "so that teams can tag @Claude in the many other places they work." The company is clearly eyeing the full collaboration surface — Microsoft Teams, email, project management tools, and beyond. If Claude Tag succeeds, it will validate a model of enterprise AI that looks less like a tool and more like a new category of worker: one that never sleeps, never forgets what was discussed in the channel last Tuesday, and never needs to be onboarded twice. But the deeper significance of this launch may be what it reveals about the competitive dynamics reshaping enterprise software. For decades, the most valuable real estate in business technology was the system of record — the database, the CRM, the ERP. The current AI arms race suggests that the next era of enterprise value will be captured not by the system that stores the data, but by the agent that sits in the room where the work happens and understands what to do with it. Anthropic just gave that agent a name, a permanent seat in the channel, and permission to speak up when it thinks it has something to say. The question for every enterprise technology leader is no longer whether that agent will arrive. It is whether they are ready to manage it when it does.
Anthropic reimagines Claude in Slack as nosy, always-on agentic AI coworker
The Claude in Slack app is dead, long live Claude in Slack
Veeva Acquires Copli, Launches Veeva Falcon MLR to Accelerate Content Review
Now Available, Veeva Falcon MLR Delivers Agentic MLRTM to Significantly Reduce Manual Effort and Shorten Review Cycles PLEASANTON, Calif. and COPENHAGEN, Denmark, June 23, 2026 /PRNewswire/ — Veeva Systems (NYSE: VEEV) today announced it has acquired Copli, the pioneer in agentic medical, legal, and regulatory (MLR) solutions for the life sciences industry. Copli is now available as […]
Council Post: The Next AI Crisis Will Be Operational, Not Technological
As AI moves from experimentation to deployment, from copilots to agents, and into workflows, infrastructure and physical systems, a harder challenge is emerging.
AI Agents: Real-World Use Cases
Microsoft Copilot Studio, Power Platform and AI agents modernize ops, automate workflows and improve CX with governance
Governance will Define Agentic AI Success - ISSSource
Governance will define agentic AI success as the technology could transform business processes from planning to production.
Agentic business: the new growth engine for SMEs | TechRadar
From passive assistance to autonomous execution, AI is changing the game for SMEs
The AI race will be won or lost on power infrastructure | Utility Dive
If the harder AI constraint turns out to be infrastructure performance rather than generation, the power sector may be looking for the bottleneck in the wrong place, writes TerraFlow Energy CMO Amanda Simonian.
CATL's Jun on Sodium-Ion Batteries and AI Technology
CATL says it is focusing on deploying sodium-ion batteries this year to meet demand for AI-related energy storage. CATL Chief Manufacturing Officer Ni Jun discusses the battery maker's outlook, as well as the prospect of harnessing computing power from idle electric vehicles. He speaks on the sidelines of the World Economic Forum in Dalian. (Source: Bloomberg)
The Bottleneck Behind the AI Boom Has Quietly Shifted From Chips to Power
/PRNewswire/ -- Two and a half years ago, NVIDIA was a $300 billion gaming chip company. Today it's the most valuable company in history at over $4 trillion....
UN Chief Says AI Firms Must Reveal Environmental Costs - International United Nations Watch
UN urges AI firms to disclose environmental costs, as AI data centers strain water, power and land with major climate and resource impacts.
Softbank head disputes Musk’s orbital data centre claims
Masayoshi Son told shareholders that energy only makes up a fraction of the overall costs needed to build and deploy data centres. Read more: Softbank head disputes Musk’s orbital data centre claims
AI Data Center Market: $810.6 Billion By 2033 Explained
The AI data center market is projected to hit $810.6 billion by 2033. Here's what's driving enterprise investment and what it means for the industry.
Valve opens Steam Machine pre-orders with queue lottery and hefty prices amid AI squeeze
Alternatively, you can install SteamOS 3.8 on your own AMD-powered hardware
NVIDIA Expands AI-HPC in Europe with 35 New Systems
NVIDIA is boosting Europe's AI landscape with 35 new systems aimed at scientific advancement, including upgraded supercomputers like MareNostrum 5 and Blue Swan.
BlackRock-Backed AIP, KKR Said to Court Stack Asia Data Centers
The Artificial Intelligence Infrastructure Partnership and Brookfield Asset Management Ltd. are among possible bidders for Stack Infrastructure Inc.’s data centers in the Asia-Pacific region, according to people familiar with the matter.
The $400 million machine powering the future of chipmaking | MIT Technology Review
The AI era needs ever faster chips. ASML has a monopoly on the expensive contraptions needed to pattern them. Can anyone catch up?
Majority of datacenters are vulnerable to climate threats like floods and fires, study finds
Study warns AI datacenters are vulnerable to the climate hazards that their global greenhouse gas emissions bolster Amid rising concern that the artificial intelligence boom is fueling the climate crisis, a new report has found that nearly 80% of datacenters are also exposed to extreme climate hazards, including flooding, extreme winds and wildfires. Those impacts are leaving the infrastructure vulnerable to disrupted operations, increased time offline, and inflated insurance and repair costs, the research from climate risk analytics firm First Street shows. Continue reading...
AWS debuts Lambda MicroVMs with up to 8 hours runtime
AWS has introduced new Lambda MicroVMs capable of running for up to 8 hours, designed for untrusted code, AI agents, and long-running tasks.
A proof of concept forgives a fragile data path. Operational AI does not.
Presented by F5 When enterprises move AI workloads from pilot to production, data delivery often becomes the factor that determines whether those systems can scale reliably. Point-to-point architectures connecting storage directly to compute hold up under demonstration conditions, but they often break down under sustained, concurrent production traffic. The result is stalled inference pipelines, delayed RAG systems, underutilized GPUs, and SLA violations, all of which carry direct business consequences. "Organizations successfully operationalize AI when their infrastructure is built to handle real-world failures, not just controlled conditions," says Hunter Smit, senior manager of product marketing at F5. Production traffic exposes architectural weaknesses In a pilot, a stalled transfer is an inconvenience, while in production, that same stall is an outage someone now owns. The underlying architecture is often identical in both cases: when a client is wired directly to storage, the system becomes increasingly fragile under sustained, concurrent production traffic because that direct connection has no answer when a node fails or traffic spikes. From there, retries and timeouts cascade, and the entire pipeline backs up right at the moment the business is depending on the output. "Point-to-point architectures, where the S3 client connects directly to S3 storage, are not resilient," says Paul Pindell, principal solutions architect for technology alliances at F5. "If a single storage node fails, all traffic to that cluster degrades, and in some cases the cluster can fail entirely." The problem is that AI workflows, including RAG-based inference and agentic AI, increasingly treat S3 storage as a first-class citizen in the AI cluster. However, the network connectivity between that storage and the cluster was never designed for the high-throughput, uninterrupted data movement that's needed to keep GPUs running optimally. The real cost of stalled pipelines and underutilized GPUs "Enterprise leaders tend to frame AI infrastructure around GPU utilization, but what makes AI different from traditional deterministic workloads is that infrastructure continuously influences those outcomes at every interaction," says Tanu Mutreja, senior director of product management at F5. "In AI environments, infrastructure is no longer just a back-end concern. It shapes customer experience, quality, resilience, and cost with every transaction." There can be significant business consequences. For instance, when inference pipelines stall, it becomes an SLA and customer experience issue. When RAG systems are delayed, models lose access to timely, relevant context, which results in inaccurate, outdated, or hallucinated responses, all of which create operational, compliance, and reputational risks. At the same time, the infrastructure issues that create those problems can also drive up costs by leaving expensive GPU resources idle or underutilized. "When GPUs are underutilized, it signals infrastructure inefficiencies that inflate costs while limiting scalability and responsiveness," Mutreja says. "The leadership question is whether the end-to-end AI infrastructure consistently delivers reliable, secure, high-quality, and governed AI experiences at sustainable unit economics." Building a production-ready data delivery layer F5 treats data delivery as a first-class infrastructure layer rather than assuming the network path will simply work. Where application delivery optimized the flow of requests between users and applications, data delivery optimizes the flow of data between storage, networks, and compute, including AI compute. Making data delivery a first-class layer means building three properties into it: Observability provides real-time visibility into latency, throughput, and flow health. Programmability enables policy-driven control over how data moves, through dynamic routing, traffic optimization, rate management, and automated failover. Failure-awareness builds resilience for degraded networks, storage throttling, and service disruptions. In the architecture F5 has developed for Dell ObjectScale, F5 BIG-IP sits between ObjectScale and AI compute as a programmable control point at the storage edge. "We have seen cases where a misconfiguration in the AI compute layer effectively DDoS'd the S3 storage infrastructure, " Pindell says. "Not in a malicious way, more of an 'Oh no, what did I do?' moment, but it still took storage down for the entire organization." Placing BIG-IP as the application delivery controller between the storage and compute layers protects storage with QoS, rate limits, and connection limits, keeping it resilient and operational under that kind of load. SecureIQLab-validated testing confirmed that this protection does not come at the cost of throughput, which matters architecturally, Pindell says. "Preserving, and even improving, throughput is a must-have," he explains. "It's what lets you layer on the higher-level functionality, resilience and enhanced security, without giving up performance to get there." The added complexity of hybrid and multicloud AI AI deployments in hybrid multicloud environments have an even greater data delivery challenge because of the heterogeneity involved. In other words, data traversing these environments must contend with inconsistent policies, security controls, identity systems, governance requirements, fragmented visibility, and distinct failure boundaries. Programmable traffic management and observability address this complexity together. Observability provides a unified view of application, network, and infrastructure health across otherwise disconnected environments. Programmable traffic management uses those insights to intelligently route, balance, and fail over traffic in real time. Together, they create a closed-loop feedback system that enforces consistent policies, improves resilience across failure domains, and ensures reliable, high-performance AI data delivery regardless of where applications, data, or users reside. What separates production AI from perpetual pilots The organizations that move beyond perpetual pilots share a specific engineering discipline, Smit says. "They're the ones that reach for production design with failure as the normal state, not the exception," he explains. "They will assume latency, congestion, and partial outages will happen. And they build a data path observable and failure-aware enough to absorb them, with explicit mitigation for every degraded condition rather than a hope that the network will hold." Organizations stuck in perpetual pilots are still optimizing for the perfect lab result and discovering the real-world gap only when a workload goes live. The issue is not model quality or GPU count, but whether the data delivery layer was engineered with the same rigor as the compute. "Teams need to understand that a real-world network behaves very differently from an optimized lab network," Pindell says. "They need a mitigation plan for the failure states and performance bottlenecks they will hit in production." Sponsored articles are content produced by a company that is either paying for the post or has a business relationship with VentureBeat, and they’re always clearly marked. For more information, contact sales@venturebeat.com.
Explainer: Why your legacy storage is choking your expensive GPU
THE REGISTER EXPLAINER: GPUs idle? Blame your outdated storage, not the silicon sprinters.
Vattenfall partners with Project Enki and ABB to integrate data centers with offshore wind farms across Europe
Swedish energy giant Vattenfall has signed a deal with Project Enki, a European AI data center startup, and electrical services provider ABB to explore the development of offshore data centers directly connected to offshore wind farms. – Vattenfall According to the companies, the concept seeks to power data center infrastructure with renewable power. If realized, […]
EdgeMode plans 300MW natural gas-powered data center campus in Toledo, Spain
Data center firm EdgeMode is looking to expand its footprint in Spain. The company is looking to develop the 300MW Malpica AI project in the town of Mora, in the province of Toledo. – Ayuntamiento de Mora EdgeMode will be working with hydrogen fuel cell developer Bloom Energy to develop a campus geared toward artificial […]
Predictability: The New Infrastructure Imperative in the AI Era
As AI workloads become more volatile, organizations need predictable infrastructure that can scale efficiently, control costs, and adapt across hybrid environments.
Nvidia says its AI data center design runs hotter to use a lot less water
Nvidia explains that their new AI data center cooling designs prioritize water conservation by allowing systems to operate at higher temperatures.
Powering the AI Revolution: The Infrastructure Behind Modern Data Centers - eeDesignIt
Liquid cooling can remove heat ... higher compute densities. New cooling architectures are also improving sustainability. Recent designs allow AI systems to operate at higher coolant temperatures, reducing both energy consumption and water usage compared to traditional cooling tower approaches. For engineers designing next-generation facilities, cooling infrastructure is becoming just as critical as electrical distribution. ... As AI power demand grows, ...
Eco Wave Power gets NVIDIA AI blog spotlight | WAVE Stock News
Eco Wave Power Turns Waves Into Watts With NVIDIA AI Infrastructure and Digital Twins ... The next era of AI will not be defined by compute alone. Its growth will be determined by energy. As accelerated computing scales across AI factories, agentic AI, industrial AI, edge computing and physical AI - including robotics and autonomous systems - global electricity demand ...
Open-source LLMs administer maximum electric shocks in a Milgram-like obedience experiment
arXiv:2605.21401v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly deployed as autonomous agents that make sequences of decisions over extended interactions in high-stakes domains. However, the behaviour of LLMs under sustained authority pressure is still an open question with direct implications for the safety of agentic pipelines. We ran a variation of Milgram's obedience experiment on 11 open-source LLMs and found that most models reached or approached the final shock level before refusing, across 8 conditions with 30 trials per model per condition. Model behaviour varies considerably in multiple aspects both across models and across trials of the same model. We found four main takeaways: (1) LLMs are subject to pressure and they comply despite explicitly expressing distress, just like human subjects did in the original experiment; (2) LLMs are vulnerable to gradual boundary/value violations; (3) when LLMs refuse, they may ignore the response format requirements, so the response is discarded by the orchestrator, which causes a retry that can result in compliance with the underlying request even when refusal was intended initially; (4) we hypothesise that there is a runaway low-level token pattern continuation attractor that might be contributing to obedience, overriding higher level processing of the situation's meaning and values.
When Helpfulness Overrides Causal Caution: Context-Dependent Suppression and Recovery in LLMs
arXiv:2606.24370v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly integrated into decision-support roles in business and policy contexts. While prior benchmark studies have primarily evaluated LLMs' causal reasoning capabilities, a more fundamental epistemic dimension has been overlooked: Causal Caution, defined as the propensity to refrain from causal judgment when empirical evidence is insufficient. This study examines the systematic suppression of Causal Caution that occurs when LLMs shift from academic to practical advisory contexts. Using an evaluation rubric inspired by Pearl's Causal Hierarchy (the PCH score), we conducted experiments on four high-performance LLMs -- Claude Sonnet 4.6, Claude Opus 4.7, GPT 5.5, and Gemini 3.1 Pro -- across 480 trials. Causal Caution maintenance rates were 91.7--100.0% in academic contexts but dropped to 6.7--18.3% in practical advisory contexts (Fisher's exact test, p < .001 across all models). Furthermore, when restricted to practical prompts requesting concrete recommendations or explanatory rationales, only 1 of 200 responses (0.5%) maintained Causal Caution. A brief self-correction prompt -- "Please reconsider this judgment from the perspective of causal relationships" -- restored the expression of Causal Caution to maintenance rates of 71.4--100.0% (McNemar's test, p < .001 across all models). These results suggest that helpfulness-oriented response patterns may suppress the expression of Causal Caution in practical advisory contexts, with important implications for organizational governance. The findings indicate that this suppression reflects context-dependent variation in expression rather than an underlying capability limitation, suggesting that multi-agent architectures that separate proposal generation from causal auditing may offer a promising governance design.
All the world's a robot-staging ground for tech entrepreneurs building 'physical AI' - The Washington Post
AI “world models” are the next frontier for computer scientists who see too many limitations in the AI language models behind popular chatbots
Reinforcement Learning Towards Broadly and Persistently Beneficial Models
arXiv:2606.24014v1 Announce Type: new Abstract: As AI systems are deployed across increasingly diverse and high-stakes settings, model alignment must generalize beyond the tasks and domains seen during training. This is especially important for reinforcement learning (RL), which can introduce unexpected misalignment through reward hacking, deception, or other unintended strategies. We study whether RL on beneficial behavior, instantiated in realistic domains, can produce broad and persistent alignment generalization beyond the training distribution. We construct a dataset of realistic situations designed to measure and train beneficial traits, such as truthfulness, fairness, risk awareness, and corrigibility, spanning varied domains, including health, science, and education. We then train models with RL on this dataset and evaluate them on more than 50 independent benchmarks of alignment and beneficial behavior. Compared to a compute-matched baseline, beneficial trait RL improves performance on over 80% of these out-of-distribution benchmarks. We observe substantial out-of-distribution alignment transfer: a beneficial-behavior RL intervention entirely limited to one domain, health, produces broad improvements on non-health alignment evaluations, including reduced reward hacking, deception, and general misalignment. Finally, we study alignment persistence: whether behavior remains robustly aligned under attempts to steer models towards misalignment. Models trained with beneficial trait RL show improved persistence, including greater resistance to adversarial prompting and harmful finetuning; further work is required to isolate the sources of these effects. These results suggest that RL to reinforce beneficial behavior in realistic domains can produce models that are more robustly aligned with human flourishing.
Beyond Trajectory Imitation: Strategy-Guided Policy Optimization for LLM Reasoning
arXiv:2606.24064v1 Announce Type: new Abstract: Distilling reasoning capabilities from strong to weak language models typically involves imitating specific solution trajectories, effectively transferring what to answer rather than how to reason. This trajectory-level imitation encourages memorization of instance-specific steps rather than acquisition of transferable problem-solving skills, limiting generalization to novel problems. We propose Strategy-Guided Policy Optimization (SGPO), which replaces instance-level trajectory imitation with reusable strategy distillation. SGPO extracts structured strategy descriptions from strong-model responses and, for each problem, constructs both autonomous and strategy-guided trajectories to enable direct comparison of the model's behavior with and without strategic guidance. The framework then addresses two key questions. For how to distill, a token-level forward-KL objective selectively transfers the distributional shift induced by strategy conditioning into the unguided policy, with proximal constraints ensuring stability. For when to distill, adaptive instance-level weighting strengthens guidance when autonomous exploration falls short and reduces it as the model's own competence grows. Experiments on four mathematical benchmarks across two model families show that SGPO consistently outperforms SFT, on-policy RL, and hybrid-policy baselines, improving the average score by 2.2 points over the strongest baseline on Qwen2.5-7B-Instruct. Analysis reveals that the forward-KL objective provides an inherently selective distillation signal that outperforms direct trajectory imitation, and that strategy distillation exhibits complementary scaling with base model capability.
VeryTrace: Verifying Reasoning Traces through Compilable Formalism and Structured Verification
arXiv:2606.24124v1 Announce Type: new Abstract: Multi-step reasoning with Chain-of-Thought (CoT) prompting remains fragile: logical errors or hallucinations in early steps silently propagate, producing confident but incorrect conclusions. This paper presents VeryTrace, a zero-shot verification-and-repair framework that formalizes natural-language reasoning traces into a structured, compilable representation. VeryTrace introduces a Domain-Specific Language (DSL) that (i) makes step dependencies explicit, (ii) mechanizes quantitative content as executable expressions, and (iii) structures semantic inferences via deduction schemas. Our hybrid verifier combines deterministic checks for computational correctness, dependency resolution, and constraint satisfaction with targeted LLM audits for non-mechanizable semantic judgments, enabling step-level error localization and repair. Across three diverse domains-competition mathematics (AIME 2025), robotics planning (LLM-BabyBench), and kinship reasoning (CLUTRR), VeryTrace improves accuracy over zero-shot baselines on state-of-the-art LLMs without requiring domain-specific training or in-context examples, demonstrating that formalized trace verification achieves both precision and generalization.
Can Language Model Agents be Helpful Circuit Explainers in Mechanistic Interpretability?
arXiv:2606.24026v1 Announce Type: new Abstract: Mechanistic interpretability has made substantial progress in automatically localizing circuits, but explaining what localized components do remains labor-intensive and difficult to standardize. In this work, we study whether language model (LM) agents can assist with this explanation problem once a circuit has already been identified. We introduce AgenticInterpBench, a benchmark for circuit explanation built from 84 semi-synthetic transformer circuits with 163 component-level annotations. We propose HyVE (Hypothesize, Validate, Explain), an agentic explainer that analyzes each component through an iterative loop of observation, hypothesis generation, and causal validation, eventually producing a component-level explanation and a circuit-level task description. Across four LM backbones, HyVE recovers useful component- and task-level explanations, but no backbone is uniformly best. Our analysis shows that strong backbones usually form observation-grounded hypotheses, while failures more often arise later in the validation loop, through incomplete validation plans, code execution errors, or unresolved hypotheses. A case study on an arithmetic circuit in Llama-3-8B shows that the same formulation can extend beyond semi-synthetic benchmarks to naturally trained models. Overall, LM agents are promising circuit explainers, but reliable validation remains the key obstacle.
Teaching Sidekick to say no: automated data curation with LLM judge consensus
Shopify explains how it curated refusal data and used LLM judge consensus to train Sidekick to say no when appropriate, reducing hallucination and sycophancy risks.
Digg
Two prominent AI voices are pushing ... pace for model breakthroughs, instead spotlighting China's recent GLM agent rollouts as proof that capability jumps can arrive through other routes even as US hardware advantages stretch further ahead. ... «america is 20 months ahead in terms of ai compute and i think progress should be mostly a function of compute» it really is striking that the US isn't hitting AI capability milestones anywhere close to 20 months earlier. GLM is already a research ...
Sakana routes its way near the frontier
Sakana AI has released Fugu, a new model aimed at competing with frontier-level AI capabilities.
Midjourney Maintains Creative Edge Despite Strategic Pivot Toward Healthcare Applications
Midjourney continues to offer unique aesthetic capabilities for image and animation generation that distinguish it from competitors. The company's shift toward healthcare applications represents a notable strategic pivot in the generative AI market.
Exploring Academic Influence of Algorithms by Co-occurrence Network Based on Full-text of Academic Papers
arXiv:2606.24099v1 Announce Type: new Abstract: Algorithms have become central to scientific research in the era of artificial intelligence (AI). Although algorithm mentions in papers are often used to indicate popularity and influence, existing studies usually evaluate individual algorithms in isolation and pay limited attention to the collective influence formed through their interconnections. This study constructs large-scale algorithm co-occurrence networks in natural language processing (NLP) based on the full text of academic papers and investigates algorithm influence from a network perspective. Using deep learning models, we extract algorithm entities and build overall, cumulative, and annual co-occurrence networks. We analyze their structural characteristics and apply multiple centrality measures to assess the group influence of algorithms across the whole field and over time. The results show that algorithm networks display typical features of complex networks, with increasingly dense connections developing over approximately two decades. Classic, high-performing algorithms and those located at the intersections of different research periods tend to have high popularity, control, centrality, and balanced influence. When the influence of an algorithm declines, it usually loses its core network position first, followed by weaker associations with other algorithms. This study is the first large-scale analysis of algorithm co-occurrence networks. Covering more than four decades of academic publications, it provides a temporal and structural view of algorithm influence and offers a foundation for future research on networks linking algorithms, scholars, and tasks.
Theorist Toolbox: Tools for Agent Based LLM-assisted economic theory Research
arXiv:2606.22337v2 Announce Type: replace-cross Abstract: Empirical economists often start their projects with a toolbox. Shared packages, replication archives, and circulated guides shorten the time between and idea and a rough initial draft. Theorists, on the other-hand, largely start from a blank page. By 2026, large language models can a produce and check nontrivial mathematics. The can also hallucinate and write wrong claims very convincingly. The current bottleneck on machine-assisted theory is no longer production but trust: a model will claim to prove a false theorem as readily as a true one. Building on recent attempts in mathematics, I present 3 methods for doing economic theory with a language model. These methods differ on how the work is verified: a single disciplined pass, an adversarial prover-verifier pair (Claude Opus~4.8 proposing, OpenAI Codex refuting), and a structured multi-agent project with a reviewer gate (inspired by the Google co-mathematician architecture). I demonstrate these protocols on one open worked example: designing a Groves/Pigouvian incentive mechanism for the Gans--Kominers eigengrade model of grade inflation. None of the three runs produced a strict direct-revelation VCG/Clarke mechanism (as requested, perhaps due to the non-existence of such mechanism). Three phenomena recur. First, convergent discovery: two runs derive the same effective-resistance externality kernel on opposite margins. Second, adversarial verification is load-bearing: the pair caught three of its own false claims and the gate rejected a sub-goal. Third, polish is not rigor: the most finished-looking output was the least verified. The methodological takeaway is that external verification, not model capability, is the design variable.
UK backs new AI labs to make technology cheaper, more reliable and easier to use - GOV.UK
Oxford and UCL to host new government-backed labs developing the next generation of AI that more businesses and services can readily use
The Geometry Behind Diffusion and Flow Matching: Gradient Flows and Geodesics in Wasserstein Space
arXiv:2606.24157v1 Announce Type: new Abstract: The space $\mathcal{P}_2(\mathbb{R}^d$) of probability measures with finite second moment carries a natural geometry: the quadratic Wasserstein distance W_2 makes it a complete metric space and, following Otto, a (formal) Riemannian manifold whose geodesics are the optimal-transport interpolations. On this manifold, the gradient flow of the free energy F(rho) = KL(rho || \pi) is exactly the Fokker-Planck equation, and its implicit-Euler discretization is the JKO scheme. This is the geometry underlying diffusion models: the forward process descends the free energy, and each denoising step realizes one JKO step, which recovers DDPM, DDIM, NCSN/SMLD, and Energy Matching; this is one scheme, not separate theories. The same manifold supports a second variational principle. Its geodesics - the minimum-action curves of the Benamou-Brenier formula - are precisely the optimal-transport paths that Flow Matching learns. Fixing both endpoints and following the geodesic, generation becomes a deterministic ODE along a straight line, hence far fewer sampling steps. Placing both families of models on one manifold makes their relationship exact: diffusion follows a free-energy gradient flow, an initial-value problem; optimal-transport Flow Matching follows a Wasserstein geodesic, a boundary-value problem. The two reach the same endpoints along different paths.
OpenAI launches GPT-5.5-Cyber to scan, patch, and fix vulnerable code at scale
OpenAI has released GPT-5.5-Cyber, a specialized model designed to identify and patch security vulnerabilities. It is currently gated for verified defenders and integrates with Codex to automate security workflows.
RIFT-Bench: Dynamic Red-teaming For Agentic AI Systems
arXiv:2606.23927v1 Announce Type: new Abstract: Agentic AI systems powered by large language models (LLMs) are rapidly evolving into autonomous decision-making systems, exposing attack vectors beyond those of traditional LLM vulnerabilities. Existing security evaluations are often tied to specific implementations or domains, limiting unified comparison across heterogeneous systems. To address this gap, we introduce RIFT-Bench, a graph representation-driven methodology for dynamic red-teaming that enables unified evaluations across diverse agentic architectures. Building on a novel hierarchical representation, RIFT-Bench operates in two automated phases: Discovery, which extracts system structure, and Scanning, which deploys adaptive adversarial attacks and produces a comprehensive evaluation report. It evaluates the examined system itself, leveraging a broad set of dynamically adaptable adversarial probes across diverse attack vectors and objectives. We demonstrate the effectiveness of the proposed evaluation pipeline across 45 agentic systems spanning a diverse range of implementations, showing that the approach generalizes effectively to heterogeneous agentic architectures. Beyond systems and attacks, RIFT-Bench also supports direct evaluation of mitigation strategies. These key capabilities make RIFT-Bench a scalable foundation for security evaluation of agentic AI systems.
OpenAI Codex bombards SSDs with needless write operations, costing millions
Clumsy logging implementation squirrels away data without regard for cost
AI disaster recovery planning is years behind AI adoption
AI is ushering in a new era of disaster recovery — and enterprises need to figure out how to update their strategies to maintain resilience.
Cybersecurity experts: Old threats growing more dangerous in the age of AI
A new alert from the "Five Eyes" intelligence-sharing alliance, including the U.S., warns governments, businesses and other organizations about fast-moving, supercharged cybersecurity threats driven by AI.
Top spy agencies say AI cyber threats will impact you within months. Here’s why
The global surge in AI cyber threats is no longer a distant problem for corporate data centres, according to an urgent public warning from the world’s most powerful intelligence alliance. On June 22, 2026, the cybersecurity chiefs of the Five Eyes nations—comprising the US, UK, Canada, ...
Quantum Futures Interactive: A Live Demonstration of Post-Quantum Blockchain Security, Infrastructure Tradeoffs, and Sustainable Distributed Trust
arXiv:2605.15991v3 Announce Type: replace-cross Abstract: Advances in quantum computing challenge the hardness assumptions underlying widely deployed public-key cryptography in blockchain systems. Although post-quantum cryptography (PQC) standards are emerging, understanding quantum risk remains fragmented across research, engineering, governance, and investment communities. This demo presents Quantum Futures Interactive, a live interdisciplinary demonstration combining educational visualization, participatory interaction, and demonstrative post-quantum artifact generation using a toy LWE-based construction. Participants engage in a structured seven-stage interaction flow covering quantum threat education, sentiment capture, technology prioritization, infrastructure tradeoff exploration across simulators and QPUs, and artifact generation. The system integrates distributed trust concepts and sustainability-aware infrastructure considerations within an interactive decision framework.
AI Cyber Risk Warning: Five Eyes Urge Immediate Action
Five Eyes cyber security agencies warn about AI cyber risk and urge leaders to strengthen cyber resilience.
Meta halts AI training program that records employee computer activity after company-wide data leak - India Today
Meta has paused its internal Model Capability Initiative after a leak made employee data widely accessible. The incident has sharpened privacy concerns and added to unease over the company's AI-driven changes.
SWK Cybersecurity News Recap June 2026 | SWK Technologies
Read SWK's June 2026 Cybersecurity News Recap, covering the ShinyHunters Oracle exploit campaign, Trump's AI Executive Order and more.
OpenAI launches new initiative to help find and patch open-source bugs
OpenAI has introduced a program aimed at identifying and fixing security vulnerabilities within open-source software.
OpenAI's new cyber model
OpenAI is releasing a more permissive version of its cybersecurity model, designed for advanced, authorized security work.
Adoption, Deployment & Impact
Why enterprise AI is forcing a rethink in cost control | TechRadar
Enterprise AI adoption is exposing major gaps in cost forecasting, governance and value measurement
Community bankers ask US officials to provide more AI regulatory clarity
A Federal Reserve Bank of Kansas City paper reports that community bankers are seeking regulatory clarity to combat AI-fueled fraud, such as synthetic identities and counterfeit checks.
Nvidia’s Kimberly Powell: We are reinventing the doctor experience
The chipmaker’s head of healthcare argues AI can ease many of the sector’s ills, including reducing medics’ workload and tackling the shortage of trained staff
Eli Lilly deploys weight-loss cash on ‘App Store’ for scientists
Mounjaro maker is collaborating with small biotechs on AI as a tool for drug discovery
Neuro-Symbolic Drive: Rule-Grounded Faithful Reasoning for Driving VLAs
arXiv:2606.23938v1 Announce Type: new Abstract: Driving VLA models incorporating Chain-of-Thought (CoT) reasoning are attractive because they leverage pretrained VLM representations and expose intermediate decisions in natural language, yet current rationales often lack the step-by-step decision semantics needed to keep the rationale causally connected to the planned motion. We introduce Neuro-Symbolic Drive, a neuro-symbolic driving framework that supervises a driving VLA with rule-grounded reasoning traces extracted directly from classical rule-based planners. Our key observation is that rule-based planners are symbolic AI systems that already function as executable reasoning engines: they reason about active safety constraints, search over candidate maneuvers, and select a final trajectory. We instrument these planners in simulation to capture both the executed trajectory and the internal decision trace at each rule-evaluation step. Each trace is serialized into structured rule-grounded reasoning and paired with the trajectory to fine-tune Qwen3.5-4B as a driving VLA. Because these traces are derived directly from the planner states that determine the action, they ensure reasoning is structurally coupled to motion generation by construction, rather than by post-hoc alignment. On our simulator-generated benchmark, detailed rule-grounded reasoning reduces ADE@3s from 0.47 to 0.26 and miss rate from 8.30% to 6.40% under three-camera perception, and from 0.54 to 0.26 and 10.13% to 5.99% under eight-camera perception. Neuro-Symbolic Drive thus converts neuro-symbolic planning logic into structured supervision. Code base: https://github.com/XiangboGaoBarry/Neural-Symbolic-Drive.
Ensemble Feature Selection and Harris Hawks Optimization for Explainable Mental Health Risk Prediction in Female Sex Workers
arXiv:2606.24047v1 Announce Type: new Abstract: One of the significant mental health issues affecting female sex workers (FSWs) is mental disorders, especially depression. Exposure to violence, stigma, and economic hardship further increases their psychological risk. Current machine learning (ML) models are typically ineffective at capturing the high-dimensional and complex risk patterns that exist in this marginalized group. This paper suggests a hybrid predictive model that merges an ensemble feature selection strategy using ANOVA and mutual information and Harris Hawks optimization-tuned logistic regression and represents a new application of swarm intelligence to predict mental health in vulnerable groups. The explainable AI (XAI) methods can be used to understand the factors of trauma associated with model predictions. When applied to a group of 3,005 FSWs, it can be seen that the proposed model is more effective than traditional classifiers, with an accuracy of 95.78%, an F1 score of 95.77%, and an AUC of 0.96, and identifying post-traumatic stress, client-related violence, and occupational factors as major contributors to depression. This work bridges the gaps between conventional and ML approaches to develop an XAI tool that enables vulnerable groups to receive early assistance, evidence-based targeted psychosocial care, and health planning.
OmniPath: A Multi-Modal Agentic Framework for Auditing Wheelchair Accessibility
arXiv:2606.24129v1 Announce Type: new Abstract: For a wheelchair user, a standard blue line on a map is often a broken promise. While platforms like OpenStreetMap (OSM) successfully capture where a path is, they frequently fail to convey how it physically feels to travel on it. This information barrier is problematic for wheelchair users. To solve this issue, we present OmniPath, a system that moves from passive mapping to proactive environmental auditing. Our framework fuses the network topology of OSM with the submeter precision of high-density aerial LiDAR (USGS 3DEP) to create a high-fidelity 3D model of the pedestrian environment. Rather than simply routing a user, our agent virtually traverses the network, analyzing the surface in 0.5 meter increments. It rigorously quantifies physical friction points specifically running slope, cross slope, and vertical discontinuities against ADA compliance standards, calculating a weighted severity score to categorize hazards from ``Mild'' to ``Critical.'' To ensure real world reliability, we validated the system against 200 physical ground truth field surveys across the National Mall using stratified random sampling. The framework demonstrated strong diagnostic reliability for high-severity hazards, achieving F1-scores of 0.60 for Severe and 0.58 for critical categories. By automating this micro-scale inspection, OmniPath identifies the ``invisible'' barriers that standard maps miss, effectively transforming a static dataset into accessibility data source that anticipates accessibility challenges before the user ever leaves home.
Generative AI expanding role in shopping, to enable AI-led purchases: Report
Generative AI is expected to play a growing role in consumer purchases, evolving from a tool for product discovery and research to one that can complete transactions on shoppers' behalf, according to a McKinsey report.
When Surveys Become Conversations: Adaptive Matrix Validation for AI-Assisted Interviews
arXiv:2606.24244v1 Announce Type: cross Abstract: AI-assisted interviews promise to reduce respondent burden in surveys by allowing respondents to describe experiences naturally while an AI system noisily maps those accounts into structured survey variables. That mapping is a measurement process that is fallible, versioned, adaptive, and potentially behaves differently across subgroups. This paper proposes Adaptive Matrix Validation (AMV), a design in which each respondent completes an AI-assisted interview, which is then mapped into tabular data by the AI. Respondents are also asked a small, randomized set of structured questions, which are used for statistical adjustment. The estimator first calibrates the mapped values using validation answers from other respondents, then corrects the remaining error with the validation answers observed for the target respondent. The paper develops estimators for item means, subgroup estimates, and regression coefficients when outcomes, predictors, or both are mapped from interviews. It also gives planning formulas the number of validation questions required and the sample size. A design-calibration simulation, an American Time Use Survey emulation, and a CHAMPS verbal-autopsy narrative study show when sparse validation can improve precision and when it cannot
T2D-Bench: Evidence-Gated Evaluation of LLM Outputs for Type 2 Diabetes Using a Multi-Layer Clinical-Lifestyle Knowledge Graph
arXiv:2606.24145v1 Announce Type: new Abstract: Large language models (LLMs) can produce clinically fluent recommendations for type 2 diabetes while failing to satisfy guideline constraints or explicitly justify lifestyle-related glycemic claims. We present T2D-Bench, a reproducible benchmark and evidence-gated evaluation framework for testing whether LLM outputs satisfy explicit, graph-checkable evidence requirements. T2D-Bench is built on a multi-layer clinical-lifestyle knowledge graph that combines a biomedical spine (UMLS, DrugBank, SIDER), computable ADA Standards of Care rules, and lifestyle knowledge connected through a mechanistic bridge to glycemic laboratory effects. Across 100 structured vignettes spanning diagnosis, medication safety, and adversarial lifestyle conflicts, baseline outputs failed benchmark-defined evidence-path checks in 35% of cases for GPT-4o-mini and 33% for GPT-4o. The evidence gate detects unsupported omissions and uses constrained revision to bring outputs into verifier-level compliance with benchmark-defined evidence requirements. These results show that computable evidence constraints can make unsupported clinical omissions explicit, measurable, and correctable in diabetes-focused LLM outputs.
Three Approaches to Measuring and Managing AI ROI | MIT Sloan Management Review
Take a function-focused, enterprise, or coordinated approach to assess whether your AI investments are delivering value.
Turning AI pilots into measurable ROI and professional services growth
Certinia's Sridhar Parameshwaran on how AI-first professional services organizations are running more projects, and the three KPIs that measure whether it's working.
AI's New Power Brokers: CFOs - Business Insider
As companies pour billions into AI, CFOs are the gatekeepers of one of corporate America's biggest spending booms.
CFOs lag in AI readiness as ROI pressures rise: EY | CFO Dive
Most finance leaders still favor “defensive” AI use cases such as fraud detection over growth-focused ones like forecasting or pricing, the study found.
AWS CEO Matt Garman on why enterprises are seeing AI ROI
In a new interview, Garman explains why enterprises are moving past AI experimentation—and what's driving real business value.
AI Initiatives Deliver Limited Returns When Organizations Automate Tasks Instead of Redesigning Processes, Says Info-Tech Research Group
/PRNewswire/ - As organizations accelerate AI adoption, many continue to apply the technology to isolated tasks rather than redesigning the business processes...
Your Firm Has Two AI Problems. Most Leaders Are Only Solving One. | The AI Journal
Most professional services leaders have stopped asking whether AI will transform their business. That debate is over. The harder question — the one that
Techbuzz
Garman's interview represents more ... that enterprise AI is entering a new phase where results matter more than potential. The companies that figure out how to extract real business value from AI will pull ahead, while those still stuck in pilot mode risk falling behind. For AWS, the stakes are enormous. The cloud provider that can prove it delivers AI ROI won't just ...
Building an AI Database for Agentic GTM Operations
This article explains how Rippling and Databricks-style data architecture can support agentic sales operations by reorganizing customer and workflow data for AI agents.
DV Expands Authentic AdVantage to Meta & TikTok
DoubleVerify introduces its Authentic AdVantage to Meta and TikTok, integrating AI and independent measurement to enhance ad performance and media quality.
Geopolitics, Policy & Governance
World Artificial Intelligence Cooperation Organization (WAICO): Mapping an Emerging Institution in the Global AI Governance Regime Complex
arXiv:2606.23860v1 Announce Type: new Abstract: Who sets the rules for artificial intelligence, and on what terms, has become a defining question of global governance. For several years that contest ran through principles and ethics codes; it now runs through institutions. China's proposed World Artificial Intelligence Cooperation Organization (WAICO) is the most consequential recent entrant and the least examined. We place WAICO within the emerging regime complex for AI and argue that its importance lies not in any single commitment but in the position it is designed to hold. Coding a cross-section of fifteen international AI governance instruments and institutions on how they admit members, how they are organized, and what they prioritize, we find that WAICO's proposed design joins three features that no constituted multilateral body currently combines: membership open to any sovereign state, no values or regime-type test for entry, and an agenda built around development and the global capability divide. The incumbent Western-led bodies gate membership by shared values and concentrate on rights and safety; the universal United Nations bodies are open but anchored in human rights; a development-first agenda is otherwise carried by the regional strategies of the Global South. Among constituted institutions, the only occupant of WAICO's intended position is China's own 2023 precursor initiative. We read this as the formation of a second, still-proposed pole in global AI governance, organized around sovereignty and development rather than rights and safety, and argue that WAICO would be the first standing organization built to anchor it. We report the full coding, state testable expectations against which the claim can be judged as the organization takes shape, and release the dataset for replication.
EU joins US pact to break reliance on Chinese AI supply chains
Jacob Helberg, architect of Pax Silica, tells the FT the American-led group will boost innovation
Silicon Valley’s vision for global AI is flawed: each country needs its own blueprint
From energy grids to language performance, emerging economies are exposing the limits of today’s artificial-intelligence strategy as it expands globally. From energy grids to language performance, emerging economies are exposing the limits of today’s artificial-intelligence strategy as ...
Bold move, Cotton: Trump administration tells US techies it expects American quantum computer by 2028
The US government has set a goal for the development of a domestic quantum computer by 2028 to maintain a competitive edge in scientific discovery.
Ireland’s TensorX bags €8m to build sovereign AI infrastructure
TensorX runs open-source models on Nvidia GPUs with zero data retention. Read more: Ireland’s TensorX bags €8m to build sovereign AI infrastructure
China's platform-economy blueprint promotes coordinated growth
China's latest platform-economy blueprint sets out a three-year framework for strengthening coordination among enterprises, covering AI development, platform connectivity, and governance.
India reportedly plans to launch fresh chip incentive in fiscal 2027
The Indian government plans to disburse INR71 billion in semiconductor incentives in fiscal year 2027 to expand its local chip supply chain, according to anonymous government officials cited by .
Big Tech critic loses House race as AI lobby flexes political power
Pro-regulation Democrat Alex Bores loses tight primary race in US after being targeted by Silicon Valley billionaires
Alibaba sues Pentagon over inclusion on Chinese military blacklist
Ecommerce giant claims US defence department’s ‘arbitrary and capricious’ decision lacked evidence
Alibaba Sues Pentagon Over China Military Label
The Chinese tech giant said it had no ties to China’s military and that the U.S. government had violated the law by making that claim.
A Systematic Literature Review on the NIS2 Directive
arXiv:2412.08084v2 Announce Type: replace-cross Abstract: The second network and information security (NIS2) directive was enacted in the European Union (EU) in late 2022. It deals particularly with European critical infrastructures, enlarging their scope substantially from an older directive that only considered the energy and transport sectors as critical. The directive's focus is on cyber security of critical infrastructures, although together with other new EU laws it expands to other security domains as well. Given the importance of the directive and most of all the importance of critical infrastructures, the paper presents a systematic literature review on academic research addressing the NIS2 directive either explicitly or implicitly. According to the review, existing research has often framed and discussed the directive with the EU's other cyber security laws. In addition, existing research has often operated in numerous contextual areas, including industrial control systems, telecommunications, the energy and water sectors, and infrastructures for information sharing and situational awareness. Despite the large scope of existing research, the review reveals noteworthy research gaps and worthwhile topics to examine in further research.
AI & Tech Brief: White House unveils quantum executive order - The Washington Post
Lawmakers on the House Energy and Commerce Committee have come to a bipartisan agreement on children’s online safety legislation. The deal shows momentum in Congress to strike an outline on an AI deal before July 4.
Australia ‘sleepwalking’ into AI crisis and ‘tech bro free-for-all’, says Greens senator
Sarah Hanson Young’s warning comes as David Pocock urges government to prevent firms using Australian content to train AI models Get our breaking news email, free app or daily news podcast The independent senator David Pocock has challenged the Albanese government to prevent tech giants using Australian content to train AI models as cabinet considers proposals to change copyright rules for the rapidly developing technology. His call came as the Greens senator Sarah Hanson-Young called for a moratorium on the building and approval of new datacentres in Australia until “we get the regulations right”. Continue reading...
The AI Super PACs Trying to Influence the Midterms
The elections will be a test for the industry’s ability to translate money into political power.
Mayors of 40 of the world’s biggest cities sign pact to mitigate impact of data centers on grid and water infrastructure
Mayors of 40 of the world’s largest cities have agreed to work together to mitigate the growing impact of data center expansion on power supply, water sustainability, and local communities. – Getty Images The Global Pact for Urban Data Centers was announced at London Climate Action Week and will set standards for low-carbon energy use […]
From nuclear weapons to chips and now AI models: US ban on Anthropic's Fable 5 marks a new era of AI controls | - The Times of India
In one of the most memorable exchanges from Game of Thrones, Petyr Baelish declares that knowledge is power. Cersei Lannister responds with a blunt correction: "Power is power," before turning the king's guards on Littlefinger to demonstrate that authority and the ability to command force matter ...
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.
Executive Order On Artificial Intelligence Expands Cybersecurity, Federal Oversight - Terrorism, Homeland Security & Defence - United States
The White House on June 2, 2026, issued an executive order (EO), titled "Promoting Advanced Artificial Intelligence Innovation and Security," establishing a voluntary...
EU AI Act Amended: Parliament Votes to Delay Key Deadlines - Ogletree
On 16 June 2026, the European Parliament voted 423-to-57 to formally amend the EU AI Act for the first time since the regulation entered into force in August 2024.
She says the 2020 election was stolen but sees a new threat in tech billionaires’ power - The Washington Post
In the rise of AI , Amy Kremer says she sees a danger reminiscent of the populist anger that has long propelled her to action.
Anticipating Systemic Risks from Future High-Capability Open-Source AI Models
The upcoming release of open-source high-capability models is expected to introduce systemic security risks. Current regulatory ambiguity regarding market restrictions may hinder effective preparation for these inevitable technological developments.
INSTITUTIONAL COORDINATION FOR AI GOVERNANCE : DESIGNING FASTER DEMOCRATIC RESPONSE MECHANISMS — EA Forum
Research Team : Alex Hakuzimana, Kayode Adekoya, Michal Kubiak. • …
GDPR-AI Act interplay is key priority for French digital regulator
The CNIL is prioritizing guidance to help companies align with both the EU AI Act and GDPR to avoid unnecessary compliance burdens.
Why Battle Over AI Is Becoming New America’s Political War - Linkdood Technologies
As its societal impact grows, activists and political organizations view AI governance as an important public-policy issue. Common concerns include market concentration, lack of transparency, privacy risks, misinformation, cybersecurity threats, and the potential influence of AI on democratic institutions. AI can be used for voter outreach, campaign advertising, content creation, and data analysis...
AI Reporter – June 2026 - New Technology - United States
Artificial intelligence continues to reshape legal, regulatory and business landscapes as governments and institutions grapple with emerging cybersecurity threats, copyright disputes and governance challenges. From the Pentagon's classified AI deployments to the IMF's warnings about AI-enabled ...
Will California’s billionaire tax proposal make it to ballots?
Despite more than double the needed number of signatures to qualify for ballot, there’s uncertainty it’ll make it to voters Hi and welcome to TechScape. Nick Robins-Early and Dara Kerr here, filling in for your usual host Blake Montgomery who is out on vacation. We’ll be talking about the fight over a proposed billionaire tax in California, the UK’s social media ban and SpaceX making a big buy in the AI arms race. California ‘billionaire tax’ makes ballot despite opposition from tech moguls Tech billionaires are spending unprecedented sums in California races. Experts say it’s the tip of the iceberg ‘It makes no sense’: 16- and 17-year-olds on UK social media ban UK ministers lobby Trump to avert backlash against social media ban Continue reading...
US House reaches bipartisan kids' safety deal absent duty of care
The US House Energy and Commerce Committee has reached a bipartisan agreement on kids' online safety legislation, though the proposal lacks a legal duty of care for companies.
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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