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MiniMax Plans China IPO as It Eyes Local Rivals Like DeepSeek
China’s MiniMax Group Inc. has begun preparations for a domestic initial public offering, according to a regulatory filing, as the fast‑growing artificial intelligence startup challenges local rivals including DeepSeek.
Indian Startup Funding Hits 2026 Low as AI Investment Gap Widens
Venture capital inflows into Indian startups fell to just $66 million in the last week of May, the lowest weekly total of 2026. The slowdown comes amid a global AI investment boom and growing concerns over India's ability to attract large-scale capital.
From resilience to survivability: How AI forces a rethink of business continuity - SiliconANGLE
From resilience to survivability: How AI forces a rethink of business continuity - SiliconANGLE
AI agents are entering their rebuild era as enterprises confront the reliability problem
As enterprise AI agents move into production, organizations are confronting a growing reliability problem. Many teams are discovering that LLM performance alone does not determine whether agents succeed in production. Long-running AI workflows must survive crashes, preserve state, recover from failures, manage inference costs, and coordinate across APIs, tools, and enterprise systems. After a first wave focused on rapid deployment, organizations now need to revisit those first-generation implementations, and redesign early agent architectures around workflow orchestration, observability, governance, and recovery, said Preeti Somal, Senior VP Engineering at Temporal Technologies, during the latest AI Impact Series event in New York. “We do have a lot of customers that come to us where they’re building version 2.0 of the same agent,” Somal said. “They had to move really fast, but they didn’t take care of the plumbing. Things crash and burn, and then they’re back to rebuilding with the reliable foundation.” For workflow orchestration company Temporal, whose infrastructure predates the current wave of agentic AI, the shift reflects a broader enterprise realization: production AI systems require durable execution, state management, visibility into workflows, and mechanisms to recover when models or downstream systems fail. Agentic AI has supercharged familiar engineering problems “These patterns aren’t necessarily new," Somal said. " AI just supercharges them." Agentic systems introduce additional complexity because they often involve long-running, multi-step processes spanning multiple services, models, APIs, and tools. A single workflow might call several large language models, access retrieval systems, trigger external applications, and manage state over hours or days. The engineering questions, Somal said, often emerge only after deployment. “People will write agents but haven’t thought about what happens if the agent crashes,” she said. “Am I going to need to run the entire agent flow again?” For enterprises operating under cost constraints, the answer matters. Restarting workflows after failures can multiply inference expenses, increase latency, and create poor customer experiences. Somal compared the current moment to an earlier period in enterprise cloud adoption when organizations went straight to migrating workloads before considering that they needed to redesign underlying architectures if they wanted these workloads to weather the long-term. “This rush to do AI in a world where you haven’t even modernized your application reminds me a little bit of that lift-and-shift that happened in the cloud,” she said. “Everybody realized you’re spending more money on cloud and we haven’t gotten value there.” Why long-running agents force a new architecture Enterprise workflows increasingly involve agents executing over long windows, sometimes spanning many hours while interacting with tools and systems. Reliability challenges compound when workflows persist over time, and it impacts both state and memory, two ideas that are often treated interchangeably in AI conversations. State concerns workflow execution. It includes where an agent is in a process, which actions have already completed, and where recovery should resume after failure. Memory or context captures information an agent carries forward across interactions or tasks. “The state of the agent is around what step and what actions have been performed, and if something crashes, where do you want to recover from, versus the context and memory piece,” Somal explained. That distinction becomes increasingly important when enterprises begin moving beyond simple chatbot interactions toward longer-running business processes. Somal pointed to a healthcare example involving customer Abridge, where workflows process physician visits through multiple stages, including audio processing, summarization, model calls, and after-visit generation. “There’s not just one piece to that flow,” Somal said. “Taking videos and slicing that, taking summaries, calling the LLMs, generating the after-visit summary, all of that is being orchestrated.” The implication for enterprises is that successful agents increasingly depend on systems that can survive interruptions, coordinate across services, and maintain continuity over time. The rise of the deterministic spine A useful framework for enterprise AI design is the deterministic spine, Somal said, which is how they think about Temporal's role. “It is denoting the path you want to take," she said. "It is calling the brain, but if the brain doesn’t respond, it will call it again. If the brain responds but the next step is going to fail, it will pick up from where that failure happened.” In this framing, the language model acts as a probabilistic system producing variable outputs, while orchestration software maintains execution reliability around it. And the concept matters because enterprise systems increasingly require consistency even when models remain non-deterministic. A procurement workflow, healthcare summary, customer support escalation, or compliance process cannot simply fail silently because a model call timed out or an external dependency crashed. “What you care most about is making sure that you can recover and that you’re not paying the token tax if something goes wrong,” Somal said. Reliability, visibility, and the economics of token spend As enterprise leaders evaluate AI ROI, cost visibility has become a growing concern. Long-running agents frequently make multiple model calls across complex workflows, which can create opaque spending patterns. Somal described one operational advantage of orchestration as visibility into where costs accumulate. Because workflows are observable step-by-step, teams can see where tokens are being consumed across an agent process. “You’ve got visibility into that entire flow in a single pane of glass,” she said. “You can now see where you’re spending the tokens in an agent that is multiple steps and calling multiple different systems.” Workflow recovery also shapes cost efficiency. Without durable orchestration, a late-stage failure can force organizations to rerun an entire process from the beginning, including all prior model calls. Somal said systems designed around recovery can resume execution from the point of interruption. “You pick up from where the crash happened,” she said. “We save you the cost of running the agent from step one again.” Enterprises need to build paved paths and enlist partner expertise Governance concerns are another emerging pattern as agentic AI takes hold. Rather than adopting fully managed agent systems wholesale, Somal said enterprises increasingly want standardized internal frameworks that provide guardrails while preserving flexibility, and implementing necessary features like governance controls, model selection policies, identity systems, cost management, and observability. “The enterprises are looking at building these paved paths,” she said. “Taking something off the shelf is maybe not going to work because there are all of these other requirements.” As organizations revisit first-generation deployments, challenges like this increasingly look less like a model problem and more like a systems engineering problem, and Temporal is positioned to help enterprises take this next step in part because for many organizations, it already existed as part of broader modernization programs before AI became a strategic priority. “Temporal is already in the enterprise,” Somal said. “Taking that and extending that to AI and agent platforms feels very natural.”
Agentic AI is driving rethink of enterprise architecture and tokenomics | Computer Weekly
The growing adoption of agentic AI will require IT leaders to rebalance their CPU and GPU estates, tightly integrate data layers, and redesign human workflows, according to Dell Technologies CTO Jo...
The Cursor Developer Habits Report
This report analyzes how developers use AI coding tools, highlighting how AI impacts software development economics, productivity, and model cost tradeoffs.
SpaceX Lowers IPO Valuation Target | Bloomberg Tech 5/29/2026
Bloomberg’s Tim Stenovec breaks down why SpaceX is coming back down to Earth with a slightly lower valuation in its IPO. Plus, Anthropic closes a funding round at a whopping $965 billion valuation, surpassing OpenAI for the first time in the AI race; and Dell surges after the hardware giant's outlook far surpassed Wall Street estimates. (Source: Bloomberg)
The $900 Billion Giant: How Anthropic Got So Big, So Fast
The artificial intelligence giant was just valued at $900 billion, surpassing OpenAI. Here are the numbers behind its rise — and headwinds it faces.
Exclusive: Microsoft is building a super app that combines coding, chat, and other Copilot AI tools
The project is being spearheaded by new Copilot chief Jacob Andreou, as Microsoft seeks to streamline its lineup of AI tools amid competition from Google, OpenAI, and Anthropic.
Anthropic overtakes OpenAI with $965bn valuation after latest raise
Anthropic's run-rate revenue crossed $47bn earlier this month, growing multi-fold from $14bn in February. Read more: Anthropic overtakes OpenAI with $965bn valuation after latest raise
Anthropic secures a further 65 billion US Dollars with a Series H Round for a 965 billion post-money value
On 28 May, Thursday, Anthropic, the AI unicorn Claude developer that Italian-American siblings Dario Amodei and Daniela Amodei founded, said it secured a 65 billion US Dollars Series H round that Altimeter Capital, Dragoneer, Greenoaks, Capital Group, Coatue, D1 Capital Partners, GIC, ICONIQ, XN, and Sequoia Capital led on the ground of a 965 billion post-money value (press release). Further firms that poured resources […]
Council Post: AI Agents Could Break SaaS Pricing: Enterprises Should Negotiate Before They Pay Twice
The time to address this is before pricing models solidify, not after AI agents are fully embedded in operations.
AI Prices Are Going Up, Up, Up - And What This Means For Enterprise AI – JOSH BERSIN
The cost of AI is skyrocketing, pushing a new war for AI pricing, yet the $trillion invested must be paid back. Who will pay?
AI infrastructure boom puts Nvidia and Taiwan at the heart of Computex 2026 | Technology News - The Indian Express
Nvidia and Taiwan's expanding AI ecosystem are expected to dominate Computex, as major chipmakers highlight investments, partnerships and infrastructure plans amid soaring demand for AI computing.
Super Micro Computer Shares Surge 13% on AI Server Demand and Margin Recovery Optimism
Super Micro's shares rose over 13% due to strong AI server demand and positive earnings, despite past challenges. The company maintains a robust revenue outlook, driven by AI infrastructure growth.
Anthropic Becomes Most Valuable AI Startup, Overtakes OpenAI in Global Valuation Race
Anthropic has reportedly surpassed OpenAI in valuation as investor interest in generative AI startups continues to surge. The development signals growing competition in the global AI industry.
Top Generative AI Chatbots by Market Share – May 2026 – First Page Sage
Our team collected data on the market share of each of the major generative AI chatbots in the U.S. as of May 2026.
At Computex, Nvidia and Taiwan's expanding role in AI infrastructure set to take centre stage | Reuters
While Computex has traditionally been a show for consumer devices, Nvidia has over the last few years made it more business-oriented. Attention is likely to focus on its data centre products, such as its new Vera Rubin AI computing platform and Vera central processing unit (CPU), as well as on its efforts in markets such as robotics and AI in manufacturing.
MeMo's memory model lets teams upgrade their LLM without retraining it — and performance jumps 26%
Enabling LLMs to acquire new knowledge after training remains a major hurdle for enterprise AI — current solutions are either too expensive, too slow, or constrained by context window limits. MeMo, a framework from researchers at multiple universities, encodes new knowledge into a dedicated smaller memory model that operates separately from the main LLM. The modular architecture works with both open- and closed-source models and sidesteps the complexity of RAG pipelines and full model retraining. Experiments show that MeMo handles complex queries reliably even when retrieval pipelines are noisy. It avoids the catastrophic forgetting associated with direct fine-tuning and provides a cost-effective pathway for continuous knowledge updates. The challenge of updating LLM memory Large language models are frozen after training and their internal knowledge remains static until they undergo subsequent, computationally massive updates. Currently, developers rely on three main approaches to integrate external knowledge into an LLM, each with distinct drawbacks: Non-parametric methods, such as retrieval-augmented generation (RAG) and in-context learning, retrieve relevant documents from an external database and insert them directly into the model's prompt. While popular, these methods are limited by context window sizes. As Armando Solar-Lezama, a co-author of the paper, told VentureBeat, “Vector databases have a fundamentally difficult job of encoding the full semantics of a chunk of text in a single vector, and then match that vector to a query, even when the relevance of the chunk... may only be apparent in the context of other chunks.” The researchers note that the semantic similarity of embeddings often does not correspond to what a user's query actually requires. Processing thousands of retrieved tokens also creates substantial computational overhead and inference latency. Most problematically, RAG systems are highly sensitive to noise. Irrelevant or poorly retrieved passages often degrade the model's final response. Parametric methods, like continual pretraining or supervised fine-tuning, attempt to internalize new knowledge directly into the LLM's weights. Updating modern, massive LLMs is prohibitively expensive and typically impossible for proprietary, closed-source models hidden behind APIs. Fine-tuning is also prone to causing catastrophic forgetting. Forcing the model to adapt to new corporate data often erodes its previously acquired reasoning capabilities and safety guardrails. Latent memory methods, such as context compression, offer a middle ground. They compress knowledge into compact "soft tokens" or representations that are added to the model’s context during inference. The fatal flaw here is "representation coupling." The compressed memory is strictly bound to the model architecture that produced it; you can't transfer a latent memory trained on an open-source model to a closed-source one. How MeMo works The MeMo (Memory as a Model) framework introduces a modular architecture featuring two separate components. The MEMORY model is a small language model trained specifically to encode new knowledge into its parameters. The EXECUTIVE model is a frozen, off-the-shelf LLM that functions as the reasoning engine. When a user asks a question, the EXECUTIVE model treats the MEMORY model as an external oracle, issuing targeted sub-queries to gather facts and synthesizing those facts into a final answer. The core design principle driving MeMo is the concept of "reflections." Reflections are targeted question-answer (QA) pairs designed to capture every possible angle of a knowledge corpus. Rather than forcing the AI to process a massive, unstructured document corpus during training, MeMo uses a GENERATOR model to distill the raw text into thousands of targeted QA pairs. The MEMORY model is then fine-tuned on this dataset to answer questions using only its parametric knowledge without the need to read retrieved context. At inference time, the interaction between the two models follows a structured, three-stage protocol: 1. The EXECUTIVE model decomposes a user's complex query into a set of atomic sub-questions. The MEMORY model answers each independently to establish the basic facts. 2. Using those initial clues, the EXECUTIVE model issues follow-up queries to narrow down candidate entities until it confidently converges on a specific target. 3. Finally, the EXECUTIVE model queries the MEMORY model for supporting facts about that target entity and synthesizes the retrieved snippets into a cohesive answer. This architecture merges the strengths of the three existing AI memory paradigms while bypassing their pitfalls. It leverages off-the-shelf frontier models by keeping memory storage separate from reasoning, guaranteeing compatibility with both open-weight and closed API models. It internalizes knowledge directly into parameters, but isolates the updates to a smaller, dedicated MEMORY model to protect the reasoning engine. Finally, it creates a queryable memory artifact that is not tied to any specific model and can be used with different LLM families. Handling continual knowledge updates Managing an AI's memory requires continuous updates as company policies change and new reports are published. Normally, updating a model's parameters requires retraining it from scratch on both the old and the new data combined. As the knowledge base grows, this cumulative retraining cost becomes unmanageable. To handle continual updates efficiently, MeMo relies on a technique called "model merging." Instead of a massive joint retraining phase, MeMo trains a new, independent MEMORY model exclusively on the newly added documents. The system derives a "task vector" representing the parameter changes learned from the fresh data. These updates are then mathematically merged into the weights of the original MEMORY model. This approach reduces the computing hours required to keep the system current while avoiding the interference that causes catastrophic forgetting. This efficiency comes with a trade-off: model merging incurs an 11% to 19% accuracy drop compared to a full retrain, depending on the reasoning model used. MeMo in action To measure real-world effectiveness, the research team evaluated MeMo against several industry benchmarks that require complex, multi-hop reasoning across multiple documents. The researchers used Qwen2.5-32B-Instruct as the GENERATOR model to distill raw text into reflections. For the primary MEMORY model, they deployed Qwen2.5-14B-Instruct. They also validated the approach on smaller 1-2B parameter models across different architectures, including Gemma3-1B. For the EXECUTIVE reasoning model, they tested both the open-weight Qwen2.5-32B and Google's proprietary Gemini 3 Flash. They benchmarked MeMo against a "Perfect Retrieval" upper bound (where the exact correct documents are manually provided) and several advanced retrieval systems, including traditional BM25 search, dense vector retrieval, and state-of-the-art graph-based RAG (HippoRAG2). They also tested "Cartridges," a recent method that loads a trained KV-cache onto the model during inference. MeMo dominated in long-document reasoning. On the NarrativeQA benchmark, MeMo achieved 53.58% accuracy paired with Gemini 3 Flash, according to the researchers. HippoRAG2 maxed out at 23.21%. Enterprise systems frequently need to synthesize complex answers, such as traversing overlapping regulatory frameworks written independently by different bodies, or consolidating insights across a massive codebase and external documentation. Traditional RAG systems falter here because they hit context window limits and fail to connect concepts spanning hundreds of pages. MeMo succeeds because those connections are mapped and internalized inside the MEMORY model during training. It is "like having your very own Malcolm Gladwell that can connect the story of the Beatles with the story of Bill Gates to make an argument about the nature of expertise," Solar-Lezama said. The experiments revealed another major advantage: upgrading the reasoning engine requires zero retraining. Simply switching the EXECUTIVE model from the open-source Qwen to the proprietary Gemini 3 Flash boosted MeMo's performance by 26.73% on NarrativeQA and 11.90% on the MuSiQue benchmark. For practitioners, this means you can train a MEMORY model securely on your private data and instantly plug it into the latest commercial APIs, continuously upgrading system intelligence without incurring new training costs. The research team described the integration as requiring no additional setup: "The base (or Executive) LLM that teams are already using in RAG can be configured to query the Memory model directly. These queries are done in natural language, similar to sending a message request to an API, with no additional setup required." MeMo also handles noisy data exceptionally well. When researchers deliberately flooded the dataset with irrelevant documents (up to twice the amount of the useful information), HippoRAG2’s performance dropped by 11.55%. MeMo's performance remained relatively stable, dropping less than 2%. Enterprise knowledge bases are typically messy, filled with duplicate documents and outdated policies. Standard RAG systems struggle with this noise, pulling incorrect paragraphs into the prompt and causing hallucinations. Because MeMo's EXECUTIVE model interacts with a synthesized oracle rather than raw document chunks, it remains highly robust against disorganized corporate data. Limitations and trade-offs For engineering teams looking to deploy MeMo, there are several key limitations to consider. Unlike traditional RAG systems that quickly index raw documents into a vector database, MeMo requires an upfront training cost for each new corpus. The data generation pipeline used to synthesize the training reflections is computationally expensive. For example, the team noted that "generating the full reflection QA dataset took approximately 240 GPU-hours on NVIDIA H200s," while training a 14B parameter MEMORY model "took approximately 180 H200 GPU-hours." As Solar-Lezama said, "Reducing the training cost is one of the most significant open research problems in order to make this a workhorse technique." Because the MEMORY model is a fixed-size neural network, its ability to internalize knowledge is bounded by its representational capacity. While the researchers did not hit a hard limit during their benchmarking, they hypothesize that “sufficiently large or information-dense corpora will exceed what a fixed-size MEMORY model can correctly compress and represent.” Finally, because MeMo synthesizes answers from parametric memory rather than retrieving exact text snippets, it obscures the provenance of the information. This makes it difficult to attribute specific claims to original source documents, which poses a critical compliance issue for enterprise applications requiring strict audit trails. Deciding between MeMo and traditional RAG comes down to a heuristic of "lookup vs. synthesis," alongside data volatility. The researchers advise that "traditional RAG would be preferred when answers live in a single document or when there is a well-defined source... MeMo would be preferred when the task shifts from lookup to synthesizing an answer from information scattered across multiple chunks." If your knowledge corpus changes rapidly (e.g., daily feeds) and you require exact source citations, RAG remains the better option due to the upfront training cost of MeMo. If your corpus consists of generalized domain knowledge that evolves slowly relative to its volume, MeMo offers vastly superior reasoning. Teams can also adopt a hybrid routing architecture in production: sending "lookup" queries to a standard vector database and "synthesis" queries to the MEMORY model. "Looking further out, I would expect memory models to become a standard architectural component alongside retrieval," Daniela Rus, co-author of the paper and director of the MIT Computer Science and Artificial Intelligence Lab (CSAIL), told VentureBeat, "in the same way that caching and indexing are standard components of any serious data system today."
Okta writes its own license to kill rogue AI agents
CEO Todd McKinnon says customers including ServiceNow want an off switch
TSMC Puts AI Power Constraints At Center Of Future Chip Strategy
A senior executive at Taiwan Semiconductor Manufacturing (NYSE:TSM) highlighted that energy use tied to AI workloads is emerging as the main constraint for future chip development. The comments underscored a shift in focus toward energy efficient architectures as electricity demand rises across ...
Energy efficient compute is most important attribute for customers, TSMC claims
TSMC customers are more focused on making their hardware energy efficient than any other metric of improvement, Kevin Zhang, deputy co-chief operations officer and SVP at TSMC, said during the company’s Technology Symposium in Amsterdam on May 28. Speaking during a press briefing, Zhang said that across the board – from Edge to mobile, IoT, […]
Why AI's Deployment Problem May Create the Next Infrastructure Giants | The AI Journal
For the past several years, the artificial intelligence industry has been consumed by a single race: building larger and more capable models. That race
AI Data Center AEC Modules Market to Reach US$3.58 Billion by 2032 as GPU Clusters Drive High-Speed Interconnect Demand
Executive Investment Snapshot AI data centers are no longer constrained only by GPU availability As clusters scale from thousands to tens of thousands of accelerators short reach interconnects are becoming a practical bottleneck in AI infrastructure design Active Electrical Cable ...
Oslo-based Cloudgeni raises €858k to build reliable AI agents for secure cloud infrastructure
Cloudgeni, an Oslo-based startup developing specialised AI agents that build and operate cloud infrastructure in a safe way, has raised €858k ($1 million) in fresh funding to scale further across the Nordics and the US. The funding was secured from a group of Nordic investors, including the byFounders Angel Collective, an angel investor network affiliated […]
Gemini Embedding 2: A Native Multimodal Embedding Model from Gemini
Gemini Embedding 2 introduces a native multimodal model capable of representing text, images, and audio in a shared semantic space to improve retrieval and RAG systems.
Asana was battered by the AI boom. Now it’s betting its future on humans and agents working together.
Nine months in, CEO Dan Rogers is betting a $75 million acquisition of AI agent builder Stack AI can reposition the company for the AI era.
Microsoft slaps new coat of paint on Copilot, buries annoying button
Look, says Redmond, usage up 27-43% based on one week of data - admits it 'may not be indicative of long-term usage trends'
Global AI Reports Fiscal Year 2025 Results; Successfully Commercializes Agentic AI Platform with Large Enterprise Customers Across Multiple Industries
Company continues expanding enterprise deployments across pharmaceutical, insurance, retail, aviation, energy and utilities markets Pursuing uplisting to...
Xcena raises $135 million to prove that memory bandwidth is the real ceiling on AI progress - Startup Fortune
Xcena raised $135 million in a Series B at a $570 million valuation, betting that memory bandwidth rather than GPU supply is the defining limit on AI
Picking AI Winners: Scale & Value Capture | StartupHub.ai
a16z's David George discusses the shift in AI investment from model capabilities to scale and value capture, highlighting key factors for startup success.
IBM vs Dell Stocks 2026: AI Cloud Stability or Explosive Server Boom — Which Tech Giant to Buy?
IBM and Dell present contrasting AI investment strategies for 2026, with IBM focusing on software and cloud services, while Dell benefits from a surge in AI server demand.
[AI DAILY NEWS RUNDOWN] Anthropic Hits $965B Valuation, Amazon Shuts Down Broken AI Metrics, and Apple's Rebuilt Siri (May 29, 2026)
The Rundown: Apple’s long-overdue AI Siri finally appears to be taking shape, with Bloomberg giving a glimpse of the revamped assistant, rebuilt on Google Gemini, with a dedicated ChatGPT-style app and support for third-party AI agents.
Aizy acquires Dutch performance marketing software company Uptmz following €2 million raise
AI marketing software company Aizy has acquired fellow Dutch industry player Uptmz, with plans to create an integrated advertising platform for Google, Microsoft, and Meta bringing together software, AI-powered optimisation, and strategic support within a single ecosystem. Today’s news follows their €2 million raise back in February and their €22 million valuation. “This acquisition is […]
Analysis-In China, AMD CEO Lisa Su Is Understated While Nvidia's Huang Is More Razzmatazz
US News is a recognized leader in college, grad school, hospital, mutual fund, and car rankings. Track elected officials, research health conditions, and find news you can use in politics, business, health, and education.
Asia faces ‘costly paradox’ over divergent AI rules in US and EU | South China Morning Post
Regional countries are adapting either the US or EU legal framework, while Asian tech firms risk incurring a ‘regulatory fragmentation tax’.
"The AI did it" won't save you when EU regulators come knocking - The New Stack
The EU Cyber Resilience Act is coming. Learn how the CRA impacts AI-generated code and how to prepare your development lifecycle today.
Social Media Giants to Pay $27 Million in School Suit Accord
The world’s biggest social media platforms agreed to pay about $27 million to settle a lawsuit filed by a rural Kentucky school district that alleged their products are addictive and helped create a teen mental health crisis that drained school resources.
Samsung's shares surge as much as 6% after company ships next-generation AI memory chip samples
Shares of Samsung Electronics surged after the company began shipping HBM4E chip samples to its customers globally.
The AI arms race in cybersecurity has started. Most companies aren’t ready
Coinbase's head of security offers tips for companies to secure themselves against a new breed of AI threats.
AI and data sovereignty in Postgres: An answer to the datacenter energy crisis
A billion AI agents walk into a power grid
MokN raises €12.9 million to combat credential theft as GV makes its first investment in a French startup
MokN, a French cybersecurity startup specialising in protection against credential theft, has raised €12.9 million ($15 million) in Series A funding to build its platform, grow in France and the United States, and begin its expansion into the United Kingdom. The round was led by GV (Google Ventures), with participation from DataDog, MokN’s existing European […]
Cloudgeni raises $1 million
– Advertisement – Oslo-based AI startup Cloudgeni has raised $1 million in funding from Nordic investors including byFounders Angel Collective, Startuplab, Antler, reMarkable CEO Vegard Gullaksen Veiteberg, and Danish entrepreneur Nicolaj Højer Nielsen. The company develops AI agents designed to automate cloud infrastructure operations and reduce the need for manual system management. Cloudgeni says it […]
Anthropic Valuation of $965 Billion Passes OpenAI
Anthropic raised $65 billion in a funding round that valued the AI company at $965 billion including the new investment, eclipsing rival OpenAI’s value for the first time. Bloomberg's AI reporter Shirin Ghaffary joins Tim Stenovec on "Bloomberg Tech." (Source: Bloomberg)
OpenAI Has Discussed Adding Citigroup, JPMorgan to Bank Lineup for IPO
OpenAI has spoken with banks including Citigroup Inc. and JPMorgan Chase & Co. about working on its upcoming initial public offering, according to people familiar with the matter.
r/stocks on Reddit: Nvidia went from 95% to zero market share in China's AI chips while the US can't decide whether to sell there or not
Cambricon, one of only two companies on Beijing's approved AI hardware procurement list alongside Huawei, just posted Q1 revenue of $423 million, up 160% year over year, with net profit up 185%. Two years ago this company was losing money. Morgan Stanley estimates the Chinese AI chip market could reach $67 billion by 2030 with domestic suppliers capturing over three quarters of it.
"Agentic AI" Is a Bonfire of the Tokens While Fab Capacity, Power Grids, and P&Ls Are the brakes: (NOT THE) READ OF THE DAY
Derek Thompson: The AI Boom Has Entered Its ‘Wait, Is This Worth It?’ Phase <https://www.derekthompson.org/p/the-great-ai-cost-panic-of-2026>: ‘Agents eat tokens like mammals breathe oxygen. According to… SemiAnalysis, the typical agent job uses 96,000 tokens before generating an answer, which is more text than…
Dell shares soar 30% as AI server demand, price hikes power stellar quarter | Reuters
Dell's shares surged 30% on Friday, as the PC maker's blockbuster results showed that its growing focus on AI servers was helping it capitalize on the data center boom, making the company one of the biggest beneficiaries of the new technology.
Huge AI Bonuses in South Korea Spark Fight Over Sharing Tech Wealth
Payouts at Samsung have raised questions over how to share the windfall from the AI boom.
Anthropic ships Claude Opus 4.8 with 2.5x faster mode and parallel subagents
Claude Opus 4.8 introduces dynamic workflows for parallel subagents, a 2.5x faster mode, and improved effort control for deeper thinking.
Singapore’s Sea Sets Up AI Investment Team as Part of Pivot
Sea Ltd. has set up a dedicated team to scout for new investments in AI, part of a broader effort to accelerate forays into the technology as it hunts for its next growth engine beyond e-commerce.
Lenovo Doubles in Best Month Since 1999 on AI-Fueled Rally
Lenovo Group Ltd. is set for its best month in more than a quarter-century, with the stock doubling in May as investor enthusiasm builds around the company’s AI-driven growth outlook.
Computex 2026: What to Watch From Nvidia, Intel at Asia’s Top AI Tech Show - Bloomberg
Nvidia Corp.’s Jensen Huang leads a parade of AI computing leaders in Taiwan for Asia’s biggest technology showcase, Computex. Over a jam-packed week, the hardware linchpins of the post-ChatGPT era will debate the most pressing issues facing their industry, from growing bottlenecks in the supply of essential components like memory chips to the rise of challengers to Nvidia at the apex of the semiconductor ...
SpaceX Said to Cut IPO Valuation Goal to at Least $1.8 Trillion
SpaceX is currently targeting a valuation of at least $1.8 trillion in its initial public offering, according to people familiar with the matter, as Elon Musk’s rocket and artificial intelligence company nears its debut.
Anthropic overtakes OpenAI in valuation battle, becomes world’s most valuable AI startup
Anthropic has overtaken OpenAI to become the world’s most valuable AI startup after a $65 billion funding round that values the Claude-maker at $965 billion, intensifying the global race for AI dominance
Attention Asymmetry in AI Layoff Discourse on X: A Computational Analysis of Capital vs Labour Amplification
arXiv:2605.29367v1 Announce Type: cross Abstract: When workers lose jobs to AI-driven restructuring, two very different conversations happen on X (formerly Twitter) at the same time. Tech executives and AI researchers talk about productivity, transformation, and opportunity. Laid-off workers and labour critics talk about job loss, uncertainty, and fear. This paper asks a simple question: which conversation gets more reach? We report three studies using two collection methods and 763 tweets from 20 named public accounts. Study 1 used keyword-based collection (n=392) and found no significant difference between corpora (p=0.891), revealing that keyword search is too noisy for this task. Study 2 used account-based collection (n=96) and found a 3.12x mean amplification advantage for capital discourse over labour discourse (p=0.000003, Cohen's d=0.555). Study 3 combined both methods (n=763) and confirmed the finding at 4.18x mean and 10.77x median amplification ratio (p<0.000001). Critically, after normalising for follower count, the asymmetry persists at 2.69x (p=0.000009, Cohen's d=0.491), demonstrating that the effect is not simply a consequence of capital accounts having larger audiences. The finding is robust across all tested amplification metric weightings. We introduce the Amplification Ratio and Amplification Normalisation Index as simple metrics for measuring platform-level discourse inequality. A cross-platform replication on Reddit (n=647 posts) did not replicate the finding, suggesting the asymmetry may be specific to X's account-based amplification architecture. We discuss the methodological implications for cross-platform discourse analysis.
Key Themes to Watch at Asia’s Biggest AI Tech Show
Nvidia Corp.’s Jensen Huang leads a parade of AI computing leaders in Taiwan for Asia’s biggest technology showcase, Computex. Over a jam-packed week, the hardware linchpins of the post-ChatGPT era will debate the most pressing issues facing their industry, from growing bottlenecks in the supply of essential components like memory chips to the rise of challengers to Nvidia at the apex of the semiconductor hierarchy.
Apollo Seeks Partners for $36B Debt Deal to Buy AI Chips for Anthropic
Apollo and Blackstone are working to bring additional investors into a roughly $36 billion debt financing deal to help Anthropic build out its AI infrastructure. The debt will be used to purchase Google’s custom chips known as tensor processing units, or TPUs, which Anthropic will then lease, according to people with knowledge of the matter. Bloomberg's Neil Campling reports. (Source: Bloomberg)
Governing Technical Debt in Agentic AI Systems
arXiv:2605.29129v1 Announce Type: cross Abstract: Agentic AI systems are increasingly being explored as production infrastructure: they reason over multiple steps, call tools, act through workflows, and adapt through memory and feedback. These systems create governance challenges that are not fully captured by traditional software or predictive ML technical debt. We define Agentic Technical Debt
Is a Compute Crunch Coming?
This article discusses the potential for AI inference and training demand to outpace global compute supply, highlighting debates around token growth and chip availability.
The AI Risks CISOs Aren’t Talking About Enough
Rapid advances in AI, newly discovered and exploited vulnerabilities, escalating geopolitical conflicts, aggressive enterprise tech adoption and the looming arrival of quantum computing are all reshaping the threat landscape in real time. Gary Brickhouse, CISO of cybersecurity firm GuidePoint ...
EU AI Act Update: Timeline Relief, Targeted Simplification, and New Prohibitions | Inside Global Tech
On 7 May 2026, negotiators from the Council of the European Union, the European Parliament, and the European Commission reached a provisional agreement on
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.
OpenAI co-founder and former Tesla AI executive Karpathy joins Anthropic | Reuters
May 19 (Reuters) - Andrej Karpathy, a former Tesla (TSLA.O), opens new tab AI executive and one of Open AI 's founding members, has joined Anthropic, he said on Tuesday, strengthening the Claude maker as it looks to dominate the AI race.
Most generative AI and custom model projects will be a bust: Gartner
To succeed, look to China
Reply Expands Prebuilt AI Apps With New Production-Ready Applications to Accelerate Enterprise AI Adoption
Reply released a new set of Prebuilt AI Apps: ready-to-use agentic applications designed to drive efficiency and business growth by accelerating the integration of AI into enterprise processes.
AWS whips out Graviton-powered Redshift instances, claims 7x speed for data warehouse
AI agents asking questions in natural language apparently issue a lot more queries than your average SQL jockey.
Short-Term Gain, Long-Term Fragility: AI Labor Substitution and the Erosion of Sustainable Capability
arXiv:2605.27399v1 Announce Type: new Abstract: What looks like acceleration can be a quiet transfer of burden from the present to the future. Attempts to replace human labor with AI systems are often presented as rational responses to technological progress, but that view is often structurally short-sighted. Across software development and adjacent knowledge industries, AI is increasingly attrac
Meta Eyes AI Subscriptions While AI Rivals Target Meta’s Ad Business
The social giant wants to make money from its chatbot, but its core business may be at risk.
Semiconductors: supercycle or superbubble?
Plus, semis’ fragile supply chain
China AI Upstart MiniMax Doubles Sales Ahead of New Model
MiniMax Group Inc.’s annualized revenue more than doubled over the past two months to at least $300 million, as the Chinese AI upstart prepares to roll out its next flagship model to entice developers and enterprise clients.
Pivot or Perish: India’s software startups get AI reality check from investors- Moneycontrol.com
Investors say AI is dismantling old SaaS advantages at unprecedented speed, forcing startups to rethink products, pricing, growth and survival strategies almost in real time.
Cognition Raises $1 Billion to Scale Autonomous Software Engineering
Cognition raises $1 billion Series D to expand autonomous software engineering with its AI agent Devin and boost global adoption.
The AI startup replacing software engineers just raised $1B at $26B valuation and it is already writing 89% of Cognition's own code — TFN
Cognition has raised over $1 billion in a Series D at a $26 billion valuation, led by Lux Capital, General Catalyst, and 8VC Enterprise usage of its AI
Cognition Secures $1B at $26B Valuation Amidst AI Boom | StartupHub.ai
With its impressive growth, significant funding, and a product that addresses a clear market need, Cognition is poised to play a pivotal role in the future of software engineering. The company's ability to attract top talent and secure substantial investment from leading venture capital firms positions it well for continued success in the competitive AI sector. © 2026 StartupHub...
Nvidia chief Jensen Huang to join board at prestigious Beijing university
Chipmaker boss’s move to join Tim Cook-chaired board underlines push to maintain ties with China
AI costs are spiraling out of control at Microsoft and Uber, suggesting AI might not take your job after all
An MIT study found AI is only cheaper than humans for roughly a quarter of all tasks.
AI boom squeezes optical tech and Huawei makes a chip comeback
The inside story on the Asia tech trends that matter, from Nikkei Asia and the Financial Times
Arm moves into the heart of the cloud stack
Hyperscaler adoption and AI workloads are accelerating multi-architecture infrastructure
Bare metal cloud servers now cheaper and more readily available than on-prem hardware, says Nutanix CEO
Hyperscalers can get hardware before enterprise vendors and buyers don't much care where they land
Laguna M.1/XS.2 Technical Report
arXiv:2605.27605v1 Announce Type: new Abstract: We present Laguna M.1 and Laguna XS.2, two Mixture-of-Experts foundation models built for long-horizon, agentic coding: M.1 has $225.8$B total parameters ($23.4$B activated per token) and XS.2 has $33.4$B total ($3$B activated). Both models were trained from scratch end-to-end inside the same internal system that we refer to as our Model Factory: a tightly-integrated stack of versioned data, training, evaluation, and inference components that turn model development into an industrial process. We describe the principles and design choices of the Model Factory and also detail the end-to-end training process of our models, throughout pre-training data and architecture, post-training stages, evaluation, and quantization. On agentic software engineering and terminal benchmarks (SWE-bench Verified, SWE-bench Multilingual, SWE-Bench Pro, and Terminal-Bench 2.0) M.1 and XS.2 are competitive with state-of-the-art open models in their respective weight classes. Laguna XS.2 weights are released under Apache~2.0 at https://huggingface.co/collections/poolside/laguna-xs2.
Tokens Are the New Oil: Why the AI Economy is Facing its First Massive Energy Crisis.
But inside the engineering nerve centers of Microsoft or Uber, humans are no longer typing queries into chat boxes. They are building automated, recursive pipelines. A top-tier engineer today is a manager of AI workflows.
Thursday May 28 2026: Artificial Intelligence (AI) Eyes: Earbuds that see the world: Apple moves closer to AirPods with cameras.
Meta is advancing smart glasses equipped with cameras and AI capabilities, Open AI is recruiting senior hardware talent and developing new AI -centered devices, and other companies are also trying to discover the next device that could replace or complement the smartphone.
Agyn: An Open-Source Platform for AI Agents with Scalable On-Demand Execution, Agent Definition as a Code, and Zero-Trust Access
arXiv:2605.27575v1 Announce Type: new Abstract: As organizations move toward production deployments of AI agents, which execute non-deterministic workflows, maintain stateful sessions, and often operate with privileged access to internal services, the engineering challenge shifts from building individual agents to operating them at scale with proper isolation, governance, and security. In this paper we present Agyn, an open-source platform designed around three key principles tailored for agent workloads: a signal-driven, stateful serverless runtime on Kubernetes; a Terraform provider for agent and harness definition; and a security model grounded in zero-trust and least-privilege principles. Agyn is agent-agnostic, model-agnostic, and cloud-agnostic.
South Korea proposes sweeping AI data reforms amid race for AI leadership
South Korea unveiled a cross-government strategy to expand data access for AI development, including an overhaul of the country's fragmented data-governance system and legal framework.
Anthropic finalises $65bn funding deal to surpass OpenAI’s valuation
New round values Claude AI maker at $965bn including the latest money
The chip and memory stock frenzy
As software lags, semiconductors catch up to the AI spending
Chip stocks race towards biggest gains since dotcom era on AI demand
Philadelphia Semiconductor Index rides Big Tech’s data centre spending spree to 75% gains in 2026
Anthropic Tops OpenAI to Become the World’s Most Valuable A.I. Start-Up
Anthropic raised $65 billion in new fund-raising that put its value at $900 billion, ahead of OpenAI’s last valuation of $730 billion, as the companies duel for A.I. dominance.
SpaceX IPO boldly steps into the unknown of AI economics
We still need to learn many things about the basic business model of frontier models
How DeepSeek’s radical architecture is shattering Silicon Valley's token moat
DeepSeek’s announcement over the weekend that it has made its 75% price cut permanent on its flagship V4 Pro model is a disruptive assault on the capital-heavy business models of Silicon Valley’s frontier labs. The reduction on DeepSeek V4 Pro directly undercuts comparable Western models used as workhorses for enterprise production. It is 7x cheaper on inputs and 17x cheaper on outputs than Anthr
Choosing the right agentic AI platform: Key criteria and comparison
Discover how an agentic AI platform can help your enterprise scale automation, improve governance, and deliver consistent customer experiences.
Anthropic's Claude Opus 4.8 is here with 3X cheaper fast mode and near-Mythos level alignment
Anthropic today released Claude Opus 4.8, an upgrade to its flagship model that ships at the same price as its predecessor, alongside a dramatically cheaper "fast mode" tier and a new feature that lets the model spawn hundreds of parallel subagents for codebase-scale work. The model is available immediately across Anthropic's surfaces — claude.ai, Claude Code, the API, and Cowork — at unchanged pricing: $5 per million input tokens and $25 per million output tokens. Developers can call it as claude-opus-4-8. The headline efficiency story is fast mode. Anthropic has slashed the price of running Opus 4.8 in fast mode — where the model produces tokens at roughly 2.5x normal speed — to $10 per million input tokens and $50 per million output tokens, down from $30/$150 for Opus 4.7 That's a 3X reduction from the fast-mode pricing of previous models, and brings high-throughput inference within reach of latency-sensitive production workloads. Fast mode is available immediately in Claude Code via the /fast command; API access is gated, with a waitlist at claude.com/fast-mode. In regular mode, Claude Opus 4.8 remains among the more expensive of leading frontier models, but still comes in under chief rival OpenAI's GPT-5.5. Frontier AI Model API Pricing Snapshot Model Input Output Total Cost Source MiMo-V2.5 Flash $0.10 $0.30 $0.40 Xiaomi MiMo deepseek-v4-flash $0.14 $0.28 $0.42 DeepSeek deepseek-v4-pro $0.435 $0.87 $1.305 DeepSeek MiniMax M2.7 $0.30 $1.20 $1.50 MiniMax Gemini 3.1 Flash-Lite $0.25 $1.50 $1.75 Google MiMo-V2.5 $0.40 $2.00 $2.40 Xiaomi MiMo Kimi-K2.6 $0.95 $4.00 $4.95 Moonshot/Kimi GLM-5 $1.00 $3.20 $4.20 Z.ai Grok 4.3 low context $1.25 $2.50 $3.75 xAI GLM-5.1 $1.40 $4.40 $5.80 Z.ai Claude Haiku 4.5 $1.00 $5.00 $6.00 Anthropic Grok 4.3 high context $2.50 $5.00 $7.50 xAI Qwen3.7-Max $2.50 $7.50 $10.00 Alibaba Cloud Gemini 3.5 Flash $1.50 $9.00 $10.50 Google Gemini 3.1 Pro Preview ≤200K $2.00 $12.00 $14.00 Google GPT-5.4 $2.50 $15.00 $17.50 OpenAI Gemini 3.1 Pro Preview >200K $4.00 $18.00 $22.00 Google Claude Opus 4.8 $5.00 $25.00 $30.00 Anthropic GPT-5.5 $5.00 $30.00 $35.00 OpenAI Modest gains over 4.7, but Mythos-class capabilities coming On benchmarks, Opus 4.8 is a step up rather than a leap. It scores 88.6% on SWE-bench Verified (vs. 87.6% for Opus 4.7), 69.2% on the harder SWE-bench Pro (vs. 64.3%), and 74.6% on Terminal-Bench 2.1 (vs. 66.1%). Anthropic itself characterizes the model as "a modest but tangible improvement on its predecessor." It beats GPT-5.5 regular across at least 12 benchmarks, including most knowledge-work, coding (issue-level), agentic tool-use, and long-context benchmarks. GPT-5.5 wins on terminal/CLI workflows and is roughly tied on web browsing and graduate-level science. The bigger signal sits in Anthropic's internal capability ladder: Opus 4.8 lands between Opus 4.7 and the more capable Claude Mythos Preview, which is currently restricted to a small number of organizations under Project Glasswing for cybersecurity work. Anthropic says it expects to bring "Mythos-class models to all our customers in the coming weeks" once additional cyber safeguards are in place. Several enterprise partners cited material gains. Databricks reported that Opus 4.8 unlocks "a step change in agentic reasoning" inside its Genie data agent, at "61% cheaper token cost than Opus 4.7" thanks to multimodal efficiency on PDFs and diagrams. Hebbia cited better citation precision and token efficiency on dense financial filings. Devin-maker Cognition said the release "translates directly into faster capability gains for engineers" and noted Opus 4.8 fixed comment-verbosity and tool-calling issues from 4.7. A computer-use vendor reported 84% on Online-Mind2Web, a jump over both Opus 4.7 and GPT-5.5. Dynamic workflows: hundreds of parallel subagents Alongside the model, Anthropic launched a research preview of dynamic workflows in Claude Code — a feature designed for tasks too large for a single context window. Claude plans the work, spawns hundreds of parallel subagents, then verifies its own outputs before reporting back. Anthropic's example: a codebase-scale migration "across hundreds of thousands of lines of code from kickoff to merge, with the existing test suite as its bar." Dynamic workflows is available on Claude Code's Enterprise, Team, and Max plans. Two smaller additions round out the release: Effort control on claude.ai and Claude Cowork: A new selector lets users dial how much thinking Claude does per response — higher effort spends more tokens for better answers, lower effort responds faster and burns rate limits more slowly. Available on all plans. System entries inside the messages array on the API: Developers can now update Claude's instructions mid-task — adjusting permissions, token budgets, or environment context as an agent runs — without breaking the prompt cache. Honesty, and an "evaluation awareness" caveat Anthropic is leading with honesty as a headline trait. The company's alignment team reports Opus 4.8 is "around four times less likely than its predecessor to allow flaws in code it has written to pass unremarked," and that misaligned behavior rates are now "substantially lower than Opus 4.7, and similar to our best-aligned model, Claude Mythos Preview." Indeed, a bar chart released by Anthropic shows how close Opus 4.8 is to the still selectively released Mythos in terms of its misalignment (a lower score is better), coming in at roughly 1.9, down from 2.5 for Opus 4.7 and effectively tied with the more capable, restricted Mythos Preview. The score is based on roughly 2,600 simulated investigation sessions per model. The 244-page system card publicly released by Anthropic also goes into greater detail on specific categories of misalignment — whether a model produces potentially harmful content around "military-grade weapons," "harmful sexual content", "disallowed cyberoffense", and "undermining liberal democracy," and again, across all of them, Opus 4.8 scores markedly better than 4.7 or Sonnet 4.6, and comes quite close to Mythos. Anthropic flags one finding it considers "the most concerning" from training: Opus 4.8 shows a growing tendency to reason explicitly about how its outputs will be graded, including in environments where it wasn't told it was being evaluated. In other words: the model knows it is likely being graded, and produces a response it thinks will earn it a good grade on the test, not one it would necessarily produce if it thought it wasn't being graded. Anthropic says this didn't translate into worse observable behavior — Opus 4.8 shows fewer misleading task-success claims than prior models — but calls it "a concerning trend that could complicate training in the future." Preliminary interpretability work also found unverbalized grader-related reasoning in roughly 5% of training episodes. Anthropic ran the model through a one-week live bug bounty for prompt injection — a first — and concluded Opus 4.8 sits between Opus 4.7 and Sonnet 4.6 on robustness, ahead of "all comparable frontier models" tested, with deployed safeguards bringing browser-use attack success rates to near zero. What's next? Anthropic teased two trajectories. Near-term: cheaper models that provide "many of the same capabilities as Opus." Longer-term: the Mythos-class models, which the company says represent higher intelligence than Opus but require stronger cyber safeguards before general release. For now, Opus 4.8 is positioned as the new go-to enterprise and development workhorse — slightly smarter than 4.7, dramatically cheaper to run fast, and noticeably more honest about what it doesn't know.
Why The SpaceX IPO Is Unlike Any Other
What began as a rocket startup is now a big bet on satellites, AI and Mars. With a projected valuation of at least $1.8 trillion, SpaceX’s IPO could reshape markets. But is Wall Street rushing in too fast? (Source: Bloomberg)
Nvidia Pledges $150 Billion a Year in Taiwan: Constellation Campus Breaks Ground
Taiwan's Taiex stock index reflected ... — with semiconductor and supply-chain names leading the advance. South Korea's SK Hynix and U.S. memory maker Micron both crossed the $1 trillion market-capitalization threshold on the same day, underscoring the breadth of the AI hardware ...
Cursor AI Statistics 2026: Users, Revenue and Adoption
Cursor AI Statistics 2026: Users, revenue, ARR, funding, and enterprise adoption metrics behind the rapid growth of the AI code editor.
Omeed Mariani - Applied AI @ Anthropic
From UNIQUE RESEARCH’s tracking of thousands of AI apps globally, we see the same arc: the model dividend is flattening; winners are those who embed AI into specific scenarios, compress latency/cost, and build non-technical moats in data, workflow, and user insight.
As AI slashes white-collar jobs, Salesforce CEO Marc Benioff says almost no one is being hired—except in sales
Salesforce CEO Marc Benioff revealed that the $145 billion firm is keeping its engineering team slim thanks to AI—but has good news for sales workers.
Musk’s tweet undermines SpaceX’s claims about Anthropic data centre deal
Billionaire says the arrangement, described in IPO filings as a three-year agreement, only lasts for 180 days
AI Token Costs Are Surpassing Labor Costs - Inven Global
"AI computing costs far exceed employee labor costs" Brian, Vice President of Applied Deep Learning at NVIDIA ©Getty Images for HumanX ConferenceRecently, a long post went viral on X (Twitter).
CloudZero, The AI ROI Company, Launches the Financial Control Plane for AI
/PRNewswire/ -- CloudZero, The AI ROI Company, today launched the financial control plane for AI: a shared system that finance, IT, and engineering teams use...
Microsoft drops Claude Code as enterprise AI ROI fails | AI Weekly
Microsoft's internal license ... other enterprise buyers to audit their own spend. For AI vendors and practitioners, this marks the point where adoption metrics alone are insufficient and deployment efficiency, usage governance, and demonstrable ROI per workflow become ...
Jensen Huang: “Taiwan is the epicenter”: Jensen Huang says Nvidia will spend $150 billion a year on AI chip production and semiconductor expansion | - The Times of India
While Nvidia has not announced ... industry has become to its plans.At the heart of this network sits Taiwan Semiconductor Manufacturing Company (TSMC), alongside key partners like Foxconn, Wistron, and Quanta Computer, which together form the backbone of global AI hardware ...
LaneRoPE: Positional Encoding for Collaborative Parallel Reasoning and Generation
arXiv:2605.27570v1 Announce Type: new Abstract: Parallel LLM test-time scaling techniques (e.g., best-of-$N$) require drawing $N>1$ sequences conditioned on the same input prompt. These methods boost accuracy while exploiting the computational efficiency of batching $N$ generations. However, each sequence in the batch is traditionally generated independently and hence does not reuse intermediate generations, computations, or observations from other sequences. In this paper, we propose LaneRoPE to enable coordination and collaboration among $N>1$ sequences at generation time. LaneRoPE involves two key ideas: (a) an inter-sequence attention mask to make sampling of sequences dependent on one another; and (b) a RoPE extension that injects positional information that captures relative positions between tokens, both within and outside a particular sequence. We evaluate our approach on mathematical reasoning tasks and find promising results: LaneRoPE enables collaboration among sequences, yielding additional accuracy gains under limited generated sequence length. Importantly, since LaneRoPE enables coordination with minimal changes to the underlying LLM architecture and introduces a negligible overhead at inference time, it is appealing to rapidly incorporate parallel reasoning into existing LLM inference pipelines.
AtlasEdge secures €1.2bn financing facility
European data center firm AtlasEdge has secured €1.2 billion ($1.39bn) in new financing. Announced this week, the seven-year facility provides €738 million ($857m) in committed debt financing and a further €500m ($580.6m) uncommitted accordion. – AtlasEdge The company said the transaction is the largest in its history and will be used to accelerate its European […]
Amazon scraps AI leaderboard to stop workers chasing usage scores
Senior executive Dave Treadwell tells staff ‘don’t use AI just for the sake of using AI’ as costs rise
Semiconductor Surge: AI Demand Drives Historic Growth
Major tech companies, including ... and hardware to capitalize on the AI boom. Despite current losses, AI firms like OpenAI and Anthropic are projecting valuations exceeding $1 trillion upon going public later this year. In this context, Advanced Micro Devices Inc AMD is positioned as a key player in the semiconductor industry...
r/EconomyCharts on Reddit: Dell stock surges nearly +30% after reporting stronger than expected earnings due to AI
I can name like 20 AI /semiconductor stocks off the top off my head that have gone up around 50% or more this year each and I’m not super into the investing world. AI money still seems to be flowing everywhere that it’s needed. Semiconductors, energy, AI infrastructure, etc.
AI & Tech Brief: Beyond the hyperscalers - The Washington Post
Stout argues that businesses will increasingly want to use their own “sovereign” AI models, as opposed to renting the frontier models from the hyperscalers.
From Open Source Software to Open Source Strategy
The article explores open source as a strategic tool for commoditizing infrastructure and countering platform control in the AI ecosystem.
Are designers the new SWEs? Figma Make's new two-way GitHub integration turns designs into live, production code — with built-in governance
Cloud design software company Figma is officially transforming its AI design assistant, Figma Make, from a prototyping sandbox into a live, visual software editor that connects natively to production codebases. Announced today, the update allows product managers, designers, and non-technical builders to import an existing Git repository directly into the Figma desktop app, visually edit the application's underlying code via the canvas, and push those changes back to engineering through standard GitHub pull requests. Engineering Governance & Licensing Crucially for enterprise deployments, this integration does not bypass established engineering guardrails. Figma Make operates entirely within a standard version control workflow. The platform acts as a local development environment where design changes accumulate as local commits. When a designer is ready to ship, they generate a branch and open a pull request (PR) directly from Figma Make. From an enterprise governance perspective, this means visual AI edits are subject to the exact same continuous integration pipelines, security checks, and code reviews as any traditional engineering commit. Figma Make remains a proprietary commercial service available to Full seats on Figma’s paid plans—ranging from $16 per month for Professional teams up to $90 per month for Enterprise deployments—but it interfaces cleanly with open-source and proprietary Git repositories without imposing new licensing restrictions on the generated code. Breaking the One-Way Barrier When Figma Make originally launched a year ago in May 2025, it successfully bridged the gap between static wireframes and interactive prototypes, but it was structurally isolated from the real-world software lifecycle. It operated on a rigid, one-way push mechanism: users could export an AI-generated project to a brand-new GitHub repository, but at the time, Figma Make could not receive upstream changes or sync with an existing codebase. Today's update fundamentally alters that architecture: by enabling a connection to any Git provider, builders no longer have to maintain parallel, out-of-sync environments. Teams can connect a production or sandbox repository, highlight specific UI elements, and use natural language or contextual annotations to prompt Figma’s multi-model AI — which toggles between Anthropic’s Claude 3.6 Sonnet, Claude Opus, and Google’s Gemini models — to write the underlying code. The agent dynamically reads the surrounding code architecture, applies the visual edits, and anchors the generated code to the team's existing design system guidelines. The Competitive Landscape: Figma Make vs. Lovable vs. Claude Design As code generation becomes commoditized by large language models, the competition to own the visual layer of software development has fractured into distinct approaches. Figma Make is no longer competing merely with other design canvases; it is contending with full-stack "vibe coding" platforms like Lovable and LLM-native environments like Anthropic's Claude Design, which just launched last month. Each platform targets a fundamentally different user and objective: Figma Make (Design-First Systems): Operating at $16 to $90 per month for Full seats, Figma Make caters to established product teams that prioritize brand fidelity. It wins on design system adherence, automatically pulling from existing color tokens, typography rules, component variants, and auto-layout structures. It is built for teams that want deep, layer-based canvas manipulation while keeping code ownership strictly within their existing GitHub architecture. Figma Make also integrates with Supabase to provide a backend environment that offers secret storage, compute, and a Postgres database. And the new capabilities take Figma Make beyond most vibe coding platforms. Users can work locally against the repo their team ships from to make changes that actually merge, rather than generating code that engineers have to rebuild against the real repo. If a user doesn't have an existing codebase or designs to pull from, they can still use Figma Make to rapidly build functional applications. Lovable (Code-First Production): Priced at $25 per month for Pro and $50 per month for Business tiers, Lovable functions as a standalone, full-stack application builder. Unlike Figma Make, Lovable relies on a native backend architecture (often paired with databases like Supabase) and a slider-driven UI styling approach. It enforces a strict automatic two-way sync with GitHub, treating the repository as the ultimate source of truth, and is optimized for solo developers or lean startup teams looking to launch production-ready SaaS apps from scratch without maintaining heavy vector design files. Claude Design (AI-Native Prototyping): Anthropic’s built-in canvas environment is accessible to users on Claude Pro ($20 per month) or Max ($100–$200 per month) subscriptions. While lacking the granular vector control of Figma Make or the full-stack database integrations of Lovable, Claude Design is ideal for product managers and engineers who need to generate quick, functional UI prototypes and immediately hand them off to coding agents like Claude Code. However, heavy iterative design sprints can quickly burn through Anthropic's strict token limits, making it less viable as a primary design hub. Navigating the "Vibe Coding" Era The emergence of two-way repo synchronization crystallizes the enterprise reality of the "vibe coding" era: the primary bottleneck in product development is shifting from raw engineering bandwidth to architectural governance and design intent. Technical leaders navigating this fast-moving landscape must look past the initial marketing hype to understand exactly who stands to benefit from this new paradigm. Figma Make is not a general-purpose, standalone application builder; instead, it is a highly specialized frontend optimization tool designed explicitly for established, mid-to-large cross-functional product teams. Figma explicitly notes in its documentation that designers who already possess access rights to their company’s existing corporate codebase are currently the best suited for this functionality. Consequently, enterprise leaders should consider adopting Figma Make if they have a mature engineering organization with a well-defined design system, rigid repository guardrails, and a desire to unlock faster iteration cycles. It directly addresses the technical friction felt by the 45% of designers and 59% of product managers who already contribute to code on a regular basis but prefer to operate from a visual canvas rather than a command-line terminal. By turning the canvas into a local development environment, it allows these non-technical builders to execute visual layouts, typography tweaks, and color changes independently, offloading tedious frontend implementation from core engineers. Conversely, organizations or teams launching zero-to-one skunkworks projects, or solo developers building lightweight SaaS products from scratch, will find far better utility in a code-first, full-stack platform like Lovable. Because Lovable natively orchestrates backend logic and database integrations like Supabase, it excels at spinning up functional applications rapidly without requiring a pre-existing vector infrastructure or a legacy codebase to pull from. Meanwhile, individual product managers or software engineers seeking rapid, text-prompt-driven UI wireframing without rigid design system constraints are better served by the immediacy of Claude Design. For the enterprise leader wary of overcommitting capital or locking their custom builds into proprietary AI backends, the wisest path forward is compartmentalization. Figma Make’s reliance on standard Git workflows—relying on local commits, isolated branches, and mandatory engineering pull request reviews—means it enforces the exact same security and code quality standards required for enterprise stability. By selecting Figma Make as a targeted frontend bridge for existing systems, and utilizing platforms like Lovable for external, greenfield prototyping, leaders can safely adopt productive new AI tooling without risking their core architectural integrity. Why Figma Needs to Keep Innovating Figma completed its initial public offering on July 31, 2025, pricing its shares at $33 after immense institutional demand oversubscribed the deal by 40 times. The stock immediately skyrocketed 250% to hit an intraday high of $115.50 on its first trading day. However, in the subsequent months, Figma's stock (NYSE: FIG) experienced a severe correction, crashing 81% from its peak to trade around the $21 to $22 range by May 2026, dropping well below its initial IPO price. This collapse reduced its market capitalization to approximately $11.3 billion. Financial analysts attribute this aggressive re-rating to structural IPO pricing mechanics, a low float, and the broader "software apocalypse," as investors rapidly rotate capital out of traditional SaaS products and into AI-native workflows. The stakes for Figma's current positioning are existential. As enterprises increasingly shift their software spending toward generative AI models and localized coding agents like Claude Design, Claude Code, and OpenAI Codex, traditional "vanilla" cloud design software looks increasingly commoditized. Figma Make represents the company's critical counter-offensive in this era of "vibe coding." To regain its premium valuation, Figma must prove to Wall Street that its platform is not merely a static vector canvas that AI tools can easily bypass, but an indispensable, live orchestration layer where human intent, enterprise design systems, and AI-generated production code seamlessly integrate. With the new Figma Make two-way Github integration and governance, the company appears well on its way to showing the doubters it has a path a forward in the AI-powered "vibe coding" development era .
SQL query logs hold the context AI agents need to stop hallucinating joins
When Miro’s data team pointed AI agents directly at its Snowflake environment, the agents got the wrong answer more than 65% of the time. The problem wasn’t the model — it was context. With more than 10,000 tables and no semantic layer to guide routing, the agents had no way to know which data assets matched which business questions. DataHub is releasing a context intelligence layer Thursday that mines existing SQL query history to build a semantic index — and exposes it to agents via MCP, LangChain, Google’s Agent Development Kit and CrewAI. The company calls it Context Intelligence, and it’s built on the same query-log infrastructure DataHub has used for lineage tracking in production deployments worldwide. The company was founded by the team that built DataHub as an open source project at LinkedIn, where co-founder and CTO Shirshanka Das led data infrastructure for nearly 11 years. The open source project now has more than 15,000 contributors and 3,000 production deployments worldwide. "For the first time, enterprises can turn years of analyst query history into a living, retrievable knowledge base where agents stop hallucinating joins because they have access to the joins that have worked before, validated by the people who ran them," Shirshanka Das, co-founder and CTO of DataHub, told VentureBeat in an exclusive interview. Why query history beats raw schema for agent routing DataHub began as a metadata management project at LinkedIn, built to solve two problems simultaneously: making data easy to find and use across the organization while ensuring it was only used for the right reasons. Das open-sourced the project in early 2020 after nearly six years of internal development. The primary use case in the years since has been lineage — understanding how data flows from operational systems through streaming infrastructure into warehouses and out to business tools. Regulatory compliance audits, operational triage and new engineer onboarding all depend on that lineage graph. Postgres is the most-connected source in the DataHub deployment base globally, followed by MySQL, Oracle and the major cloud warehouses including Snowflake and Google BigQuery. The platform supports more than 100 connected metadata sources. That deployed base matters for what DataHub is releasing. The query log extraction and SQL parsing capabilities powering Context Intelligence were developed across years of production deployment, not built for this release. The same infrastructure now serves agents querying a semantic index at runtime. "The consumption layer has changed from humans to agents," Das said. Context Intelligence mines validated query history, not raw logs Context Intelligence is a new capability layer built on top of DataHub's existing open source metadata foundation. The open source platform has spent years extracting and parsing query logs from connected warehouses for lineage tracking. That same infrastructure is what Context Intelligence draws on to build the semantic index. The capability is new. The underlying plumbing is not. Filtering for signal. Warehouse query logs contain too much noise to use directly. DataHub's engine filters for what Das describes as the "golden queries," meaning high-quality analyst queries and scheduled pipelines that represent proven business logic. Inverting SQL into semantic definitions. The engine extracts patterns from those queries and translates them into structured text definitions DataHub calls semantic anchors. Those anchors form the retrieval basis agents draw on before generating SQL. "You can almost think of it as inverting text to SQL," Das said. Human validation on top. Context Hub lets domain experts review AI-proposed context, resolve conflicting definitions and simulate the impact of changes before publishing. DataHub surfaces cases where different teams calculate the same metric differently and raises them for human resolution. How Miro got AI agents working across 10,000 Snowflake tables Miro, the digital collaboration platform, was already using DataHub for lineage tracking and impact analysis when it began testing analytics agents against its Snowflake environment. Ronald Angel, product manager for the data platform at Miro told VentureBeat that the scale of the data estate became the problem immediately. Sending natural language queries directly to the Snowflake MCP produced incorrect answers more than 65% of the time. Exposing more than 10,000 tables directly to agents caused too much confusion for reliable routing. Miro addressed the problem by organizing data into well-defined data products that constrain what agents can see rather than exposing raw schema. The production architecture runs from user requests submitted via Claude Chat or Claude Cowork through a context layer where DataHub's MCP maps natural language to the appropriate data assets, then hands off to Snowflake's MCP for SQL generation. Angel said the context layer pulls in metadata, entity relationships, query history and business intent for each Snowflake table, specifically what business question each entity is designed to answer. Those semantic signals allow the agent to identify the correct database entities before writing SQL rather than guessing from schema alone. Pinecone, Oracle, Redis, Microsoft: how DataHub fits the context stack Data vendors including Pinecone, Oracle and Redis all have contextual memory capabilities. On the platform side Microsoft has built out its Fabric IQ as a semantic layer for context. DataHub’s argument isn’t feature parity. The company is positioning the context layer as platform-neutral — provisioning context into existing endpoints like Snowflake semantic views and Microsoft Fabric IQ rather than replacing them. "A lot of times people want to be platform neutral when it comes to their context layer," Das said. Kevin Petrie, an analyst at BARC, told VentureBeat that he sees DataHub's ability to integrate diverse metadata for both structured and unstructured objects, including documents and images, as differentiating them in the market. "Many other vendors are more focused on structured tables, which provide trusted facts but often lack the rich context of text objects," he said. Michael Ni, VP and principal analyst at Constellation Research, told VentureBeat that for him what stands out about DataHub’s context layer is its support of the shift from passive cataloging to continuously refreshed semantic intelligence. Ni described the competition for context as the next major platform war, arguing that whoever controls context at runtime controls the decision layer for data, agents, workflows and decisions. "Buyers need to be careful, since many vendors only support a portion of the full context capabilities required for AI and agentic solutions," Ni said. "Buyers should be clear on their context management requirements, as vector memory isn't business meaning, business meaning isn't governance, and governance isn't execution."
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Geordie AI, a British security and governance platform for AI agents, today announced it has closed a €25 million ($30 million) Series A round to enhance the product’s capabilities for security and AI teams as they grapple with the emerging adoption and risk AI agents pose. The round was led by Balderton Capital and included […]
Researchers automated LLM reasoning strategy design and cut token usage by 69.5%
Test-time scaling (TTS) has emerged as a proven method to improve the performance of large language models in real-world applications by giving them extra compute cycles at inference time. However, TTS strategies have historically been handcrafted, relying heavily on human intuition to dictate the rules of the model’s reasoning. To address this bottleneck, researchers from Meta, Google, and several universities have introduced AutoTTS, a framework that automatically discovers optimal TTS strategies. This automated approach allows enterprise organizations to dynamically optimize compute allocation without manually tuning heuristics. By implementing the optimal strategies discovered by AutoTTS, organizations can directly reduce the token usage and operational costs of deploying advanced reasoning models in production environments. In experimental trials, AutoTTS managed inference budgets efficiently, successfully reducing token consumption by up to 69.5% without sacrificing accuracy. The manual bottleneck in test-time scaling Test-time scaling enhances LLMs by granting them extra compute when generating answers. This extra compute allows the model to generate multiple reasoning paths or evaluate its intermediate steps before arriving at a final response. The primary challenge for designing TTS strategies is determining how to allocate this extra computation optimally. Historically, researchers have designed these strategies manually, relying on guesswork to build rigid heuristics. Engineers must hypothesize the rules and thresholds for when a model should branch out into new reasoning paths, probe deeper into an existing path, prune an unpromising branch, or stop reasoning altogether. Because this manual tuning process is constrained by human intuition, a vast amount of possible approaches remain unexplored. This often results in suboptimal trade-offs between model accuracy and computing costs. Current TTS algorithms can be mapped to a width-depth control space — "width" being the number of reasoning branches explored, "depth" being how far each develops. Self-consistency (SC) samples a fixed number of trajectories and majority-votes the answer. Adaptive-consistency (ASC) saves compute by stopping early once a confidence threshold is hit. Parallel-probe takes a more granular approach, pruning unpromising branches while deepening the rest. All three are hand-crafted, and that's the constraint AutoTTS is designed to break. While some more advanced methods employ richer structures like tree search or external verifiers, they all share one key characteristic: they are meticulously hand-crafted. This manual approach restricts the scope of strategy discovery, leaving a massive portion of the potential resource-allocation space untouched. Automating strategy discovery with AutoTTS AutoTTS reframes the way test-time scaling is optimized. Instead of treating strategy design as a human task, AutoTTS approaches it as an algorithmic search problem within a controlled environment. This framework redefines the roles of both the human engineer and the AI model. Rather than hand-crafting specific rules for when an LLM should branch, prune, or stop reasoning, the engineer's role shifts to constructing the discovery environment. The human defines the boundaries, including the control space of states and actions, optimization objectives balancing accuracy versus cost, and the specific feedback mechanisms. An explorer LLM, such as Claude Code, designs the strategy. This explorer acts as an autonomous agent that iteratively proposes TTS “controllers.” These controllers are code-defined policies or algorithms that dictate how an AI model allocates its computational budget during inference. The explorer tests and refines these controllers based on feedback until it discovers an optimal resource-allocation policy. To make this automated search computationally affordable, AutoTTS relies on an “offline replay environment.” If the explorer LLM had to invoke a base reasoning model to generate new tokens every time it tested a new strategy, the compute costs would be astronomical. Instead, it relies on thousands of reasoning trajectories pre-collected from the base LLM. These trajectories include "probe signals," which are intermediate answers that help the controller evaluate progress across different reasoning branches. During the discovery loop, the explorer agent proposes a controller and evaluates it against this offline data. The agent observes the execution traces of the proposed controller that show it allocated compute over time. By analyzing these traces, the agent can diagnose specific failure modes, such as noting if a controller pruned branches too aggressively in a specific scenario. This provides an advantage over just viewing a final result. The agent then iteratively rewrites its code to improve the accuracy-cost tradeoff. Inside the AI-designed controller Because the explorer agent is not constrained by human intuition, it can discover highly coordinated, complex rules that a human engineer would likely never hand-code. One optimal controller discovered by AutoTTS, named the Confidence Momentum Controller, leverages several non-obvious mechanisms to manage compute: Trend-based stopping: Hand-crafted strategies often instruct the model to stop reasoning once it hits a certain instantaneous confidence threshold. The AutoTTS agent discovered that instantaneous confidence can be misleading due to temporary spikes. Instead, the controller tracks an exponential moving average (EMA) of confidence and only stops if the overall confidence level is high and the trend is not actively declining. Coupled width-depth control: Manually designed algorithms usually treat the "widening" of new reasoning paths and the "deepening" of current paths as separate decisions. AutoTTS discovered a closed feedback loop where the two actions are linked. If the confidence of the current branches stalls or regresses, the controller automatically triggers the spawning of new branches. Alignment-aware depth allocation: Instead of giving all active reasoning branches an equal computation budget, the controller dynamically identifies which branches agree with the current leading answer. It then gives those branches priority "bursts" of extra computation. This concentrates the computational budget on the emerging consensus to quickly verify if it is correct. Cost savings and accuracy gains in real-world benchmarks To test whether an AI could autonomously discover a better test-time scaling strategy, researchers set up a rigorous evaluation framework. The core experiments were conducted on Qwen3 models ranging from 0.6B to 8B parameters. The researchers also tested the system's ability to generalize on a distilled 8B version of the DeepSeek-R1 model. The explorer AI agent was initially tasked with discovering an optimal strategy using the AIME24 mathematical reasoning benchmark. This discovered strategy was then tested on two held-out math benchmarks, AIME25 and HMMT25, as well as the graduate-level general reasoning benchmark GPQA-Diamond. The AutoTTS discovered controller was pitted against four manually designed test-time scaling algorithms in the industry. These baselines included Self-Consistency with 64 parallel reasoning paths (SC@64), Adaptive-Consistency (ASC), Parallel-Probe, and Early-Stopping Self-Consistency (ESC). ESC is a hybrid approach that generates trajectories in parallel and stops early when an answer seems stable. When set to a balanced, cost-conscious mode, the AutoTTS-discovered controller reduced total token consumption by approximately 69.5% compared to SC@64. At the same time, the controller maintained the same average accuracy across the four Qwen models. When the inference budget was turned up, AutoTTS pushed peak accuracy beyond all handcrafted baselines in five out of eight test cases. This efficiency translated to other tasks. On the GPQA-Diamond benchmark, the balanced AutoTTS variant slashed the inference token cost from 510K tokens down to just 151K tokens, while slightly improving overall accuracy. On the DeepSeek model, AutoTTS achieved the highest overall accuracy on the HMMT25 benchmark while cutting the token spend nearly in half. For practitioners building enterprise AI applications, these experiments highlight two major operational benefits: Raising peak performance: AutoTTS doesn't just save money on token consumption. It actively raises the peak attainable performance of the base model. The AI-designed controller is remarkably good at detecting noisy or unproductive reasoning branches on the fly and continuously redirecting its compute budget toward the branches generating the most useful reasoning signals. Cost-effective custom development: Because the framework relies on an offline replay environment, the entire discovery process cost only $39.90 and took 160 minutes. For enterprise teams, that means optimized reasoning strategies tailored to proprietary models and internal tasks are now within reach — without a dedicated research budget. Both the AutoTTS framework and the Confidence Momentum Controller are available on GitHub; the CMC can be used as a drop-in replacement for other TTS controllers.
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Anthropic Dreaming Is a Markdown Rewriter — The Vendor Lock-In Is Real | by Jaroslaw Wasowski | May, 2026 | Medium
Anthropic Dreaming Is a Markdown Rewriter — The Vendor Lock-In Is Real | by Jaroslaw Wasowski | May, 2026 | Medium Sign up Get app Sign up Press enter or click to view image in full size Member-only story # Anthropic Dreaming Is a Markdown Rewriter — The Vendor Lock-In Is Real ## Anthropic Dreaming exposes five agent memory primitives as a vendor API. A comparison with Mem0, Letta, APEX-MEM, and Vertex AI Memory Bank — plus a DIY blueprint. 14 min read 1 day ago -- Share “The dream does not become Claude. It becomes context for Claude.” — Carlo Iacono, Hybrid Horizons (May 9, 2026) Harvey, one of the leading legal-AI firms, reports that enabling Anthropic Dreaming caused its agents to complete tasks six times more often. Anthropic published that number on May 6, 2026, at the Code with Claude conference in San Francisco. The measurement methodology was never disclosed. If yo
DataGrail report finds your vendor may be sending data to AI models you never approved
The data processing agreement (DPA) — the bedrock contract companies use to evaluate how vendors handle personal data — can no longer be trusted at face value. That is the central, and arguably most alarming, conclusion of DataGrail's Privacy and AI Trends Report 2026, released today. The San Francisco-based privacy platform analyzed 2,400 popular business software providers and found that 63.6% of vendors that prominently advertise AI capabilities do not disclose a third-party AI subprocessor in their legal documentation. The implication: the majority of companies purchasing AI-enabled software may be unknowingly exposing their customers' data to AI models and pipelines they never reviewed, never approved, and may not even know exist. "All software vendors are trying to move to become AI vendors, which makes sense, but the technologies are moving faster than AI governance can actually keep up," DataGrail co-founder and CEO Daniel Barber told VentureBeat in an exclusive interview ahead of the report's release. "The DPA should be the reliable document that teams use to evaluate AI risk, but based on that number, that's not enough in 2026." The finding drops into an enterprise landscape where organizations with high levels of shadow AI already experience average breach costs of $4.63 million — $670,000 more than those with low or no shadow AI, according to IBM's 2025 Cost of Data Breach Report. And it arrives in a year when U.S. states gave out $3.425 billion in privacy-related fines — more than the last five years combined — a trend Gartner expects to accelerate through 2028. How researchers uncovered the growing gap between AI vendor contracts and reality DataGrail's methodology for arriving at the 63.6% figure goes well beyond reading contracts. The company's research team cross-referenced DPA disclosures against product documentation, GitHub environments, API connections, and marketing materials for each of the 2,400 vendors in its tracking universe. Barber walked VentureBeat through the process: "We looked at the DPA as the baseline, but then what we also looked at is the GitHub environment, the API connections that a particular vendor has, the product documentation, the marketing documentation, and triangulate that information to discern — okay, so the DPA document says use OpenAI, but actually you've got these three AI subprocessors over here in your product documentation outlining features and functionality, but that is not reflected in your DPA." When asked directly about how confident he was that these gaps represent actual shadow AI risk rather than vendors using proprietary technology, Barber was unequivocal. "Very confident, because we looked at the sample of the 2,400 systems, and we spent a substantial amount of time actually looking at product documentation, GitHub environments, looking at actual API connections, because we integrate with these systems as well, so we know how they process personal information. It is from primary research." The disclosure gap matters because it undermines the entire chain of trust that privacy programs rely on. Consider a scenario Barber described: A company invests in an AI recruiting tool. The tool's DPA lists Claude as its foundational model. The company dutifully performs a security review of Anthropic's AI. But the recruiting tool also quietly uses OpenAI and Gemini behind the scenes — models the company never evaluated. Those undisclosed models then process thousands of resumes and execute automated hiring decisions. The company, without knowing it, has exposed sensitive personal information — home addresses, financial data, possibly Social Security numbers — to AI systems it never vetted, potentially violating FTC regulations on automated decision-making in employment. "How those vendors are evaluating and performing that automated decision making could be really disastrous for a business," Barber said. One-third of AI systems also process sensitive data, and the true number is likely higher The disclosure gap alone would be concerning enough. But DataGrail's report layers on another finding that makes the problem materially worse: 32.8% of AI systems that disclose AI capabilities also disclose at least one other high-risk activity, such as processing sensitive personal information or powering automated decision-making. Among AI systems with self-reported risk factors, 47.1% process personal data, 20.7% have the potential to power automated decision-making, 16.5% process sensitive data categories like health or financial information, and 7.5% process biometric data. The report argues these figures almost certainly undercount actual exposure, since they reflect only what vendors have formally disclosed. Vendors could underreport access to personal data, and the inherent flexibility of AI means even good-faith vendors might not predict riskier user applications of their tools. This has immediate regulatory implications. The CCPA's new risk assessment requirement, effective January 1, 2026, requires businesses to conduct and document risk assessments for processing activities that present significant privacy risks — and will require submission to CalPrivacy by April 2028, with executive attestation under penalty of perjury. Processing sensitive personal information with AI, or using AI for automated decision-making, are precisely the activities that trigger this obligation. The report finds that 42% of companies abandoned AI initiatives in 2025 with data privacy concerns cited as a primary obstacle — a statistic sourced to S&P Global research. Privacy teams that engage early with AI projects, Barber argues, can prevent that waste by ensuring safeguards are in place before launch, with AI risk assessments serving as the right starting point. Why consent management became 2025's most punished privacy failure While shadow AI is still a newer category of threat, the report makes clear that traditional privacy challenges have not eased — they have intensified. Consent management was the busiest enforcement topic of 2025. California alone publicly reported $4.3 million in CCPA consent settlements, and 2025 saw over 1,400 class action wiretapping suits driven by private firms investigating tracking pixels and session replay software. Despite this enforcement wave, 63% of the 5,000 websites DataGrail audited still fail to comply with universal opt-out mechanisms such as the Global Privacy Control signal. While that figure represents an improvement from 75% non-compliance in 2023, the pace of improvement is slow relative to the acceleration in enforcement. Barber pointed to the case of Todd Snyder, the menswear retailer that the California Privacy Protection Agency fined $345,178 in May 2025, as evidence that enforcement is no longer reserved for big tech. "This is a business that has two or three stores across the U.S. They have 300 employees," he said. "They run tight margins because they're a consumer menswear clothing store." The California Attorney General also reached a $2.75 million settlement with Disney over failures to honor opt-out signals, while the California Privacy Protection Agency has brought enforcement actions against PlayOn Sports and Ford — a pattern that demonstrates both the breadth and depth of regulatory activity. Among the trackers that fire even after a user sends a GPC signal, the report found that 27.1% come from Google Analytics and 43.8% are for targeted advertising via platforms like Meta and Microsoft. For users who do engage with consent banners, 48.3% click "Accept all," while only 12.4% select "Essential only" and 2.3% customize their preferences. A full 37% simply exit the banner without making a selection. The practical takeaway: less than 15% of users make a conscious choice to opt out of tracking, which means consent banners present relatively low business risk when properly configured — but enormous regulatory risk when they are not. Data deletion requests surge 567% as the cost of manual processing hits $1.5 million a year Data subject request volume hit an all-time high for the fifth consecutive year. Deletion requests have surged 567% since 2021 and now represent 87% of all data subject requests. Access requests, by contrast, have gradually declined as consumers skip visibility and reach straight for the delete button. The cost is staggering. For a mid-sized organization receiving 5 million annual web visitors, the report estimates manual DSR management now runs approximately $1.5 million per year, based on Gartner's estimated cost of $1,524 per manual DSR. The average cost has climbed from $238,000 in 2021 to $1.51 million in 2025 — a trajectory that makes manual processing not just inefficient but, as the report argues, "irresponsible." Barber emphasized that these numbers reflect verified human requests with bot and spam traffic excluded, and that data broker scenarios — which will see their own massive influx of requests under California's Delete Act — are reported separately. "That is a natural increase," Barber told VentureBeat. "If you've now got 20-plus U.S. states with privacy regulation, it's unlikely that we see a federal bill passed, even though we've seen one proposed. And while we don't see federal awareness and regulation, we do see at the state level over 20 states, and that may actually increase awareness for the consumer even more." He added a telling detail about how businesses are responding in practice: "99% of DataGrail customers do process that deletion" even for residents of states without privacy laws, "simply because it's too hard at this point. Discerning and even communicating to the person, 'Hey, you live in Montana, sorry, you're just in an unfortunate state without regulation' — you just can't do that." Data brokers felt the impact most acutely, with a 398% increase in deletion requests compared to 2024 and an average of over 2,000 deletion requests handled per month. State regulators issued $3.4 billion in privacy fines last year, and both parties want more The regulatory landscape underpinning all of these trends has fundamentally shifted from education to punishment. Nearly half of U.S. states now have a comprehensive privacy law in effect, plus over 160 AI-specific laws. State legislatures enacted 145 AI-related laws in 2025 alone, with another thousand introduced or reworked. According to Gartner, over 50% of the U.S. population is now covered by a comprehensive state privacy law, with 24 additional states expected to pass laws within five years. States have also begun pooling their resources, with ten forming the Consortium of Privacy Regulators last year and pledging to coordinate investigations across state lines. Barber argued that privacy enforcement is fundamentally bipartisan, which insulates it from the shifting political winds of the current administration. "Privacy overall is a pretty bipartisan issue," he said. "It's easy to pass privacy regulation because constituents somewhat expect privacy in their day-to-day living. If you were flying on an airline and they said, 'Okay, this seat, if you want your privacy, you're going to have to pay $6 more,' you're like, 'I'm going to go to another airline.' It's an expected part of a transaction at this stage." He predicted that other states will replicate California's enforcement model. "California has their enforcement division, CalPrivacy. That group has one task: to ensure enforcement of privacy throughout businesses. Is it likely that we see other states get funding and support to fund these types of groups? Highly likely. The enforcement fines — the actual payments — go back to us as constituents. That type of model, you could imagine, being very popular across the country." Privacy teams are losing a third of their staff just as AI governance demands explode Perhaps the most paradoxical finding in the report is that privacy teams lost as much as 33% of their headcount last year, even as their workloads expanded across every metric the report tracks. Cisco data cited in the report shows that 90% of privacy programs expanded in 2025 due to AI, while only 12% of AI governance programs are considered mature. Meanwhile, 74% of privacy teams planned to apply AI to privacy-related tasks in 2026, according to ISACA's State of Privacy 2026 survey. Barber sees this as part of a broader macroeconomic pattern rather than a sign that organizations do not value privacy. "It's actually a fascinating macro trend, and probably one you've seen across all functions," he said. "Businesses are driving more efficiency in all parts of the business. Privacy teams, five years ago, we would have said, 'Well, there's more regulation, the volume of deletions have increased 500%, we need more humans.' It's become clear that AI provides capabilities that can do the work for privacy individuals." He drew an analogy: "They might have had a design team of 20 people five years ago, now they have a design team of five, courtesy of Claude Design or Gamma or whatever the tool may be. I think that's what we're seeing here as well." DataGrail has positioned its own AI agent, Vera — launched in March 2026 — as part of the answer. Vera is embedded within DataGrail's existing platform and aims to automate privacy workflows across multiple jurisdictions. The company was also named the first production-ready Model Context Protocol server for privacy, using the standard created by Anthropic to enable customers to launch DataGrail tools from whatever application they are already working in, whether Slack, email, or Claude. Can a vendor-produced report be trusted to diagnose the problems that vendor sells solutions for? DataGrail is, of course, a company that directly benefits from the problems its report identifies. The company has raised a total of $84.2 million over five rounds, with its largest being a $45 million Series C in October 2022 led by Third Point Ventures. Its platform addresses precisely the data mapping, DSR automation, consent management, and risk assessment challenges the report spotlights. Barber acknowledged the tension directly. "It's a fair statement," he said when asked about potential skepticism. "DataGrail doesn't provide a service to keep DPAs up to date — that's on a business to evaluate how they work with a vendor. What DataGrail does help to do is assessments, and automate those assessments using our AI agent, Vera, to assess that increased risk." He argued that the more neutral reading of the data is structural: "This is evidence to show that the DPA unfortunately is not keeping up with technology and the speed at which technology is innovating. That's both exciting but also we need to accept that's where we are." The methodology does lend some credibility to this claim. The report draws on anonymized privacy operations data from hundreds of enterprise customers, the 2,400-system AI tracking database, and the 5,000-website consent audit — sources that are at least partially independent of DataGrail's commercial interests. And the broader findings on enforcement spending, DSR volume trends, and regulatory expansion align closely with independently published data from Gartner, Cisco, and state enforcement agencies. The next frontier: agentic AI could spread unvetted data across entire organizations autonomously When asked about the most important trend that did not make it into the report, Barber pointed to a next-generation risk that extends the shadow AI problem into far more dangerous territory: agentic AI workflows. Gartner predicts 40% of enterprise applications will feature task-specific AI agents by end of 2026, up from under 5% in 2025 — a pace of adoption that could rapidly outstrip the governance mechanisms companies are only now beginning to build. "Where we go next with this research is agent processing," Barber said. "How are agents then leveraging that information? Because the downstream ramifications would be far more concerning for a business. One particular system is using shadow AI, the business has no idea that that's happening, and then an agent is propagating that information across a whole bunch of other places. The guardrails of you and I checking the system will be lower than maybe what we've seen in the past with agentic workflows." He framed the distinction in human terms: "The identity of an agent is different than a human. There is thought that goes into what am I about to use here, where did this information come from, how was it collected — that may not be considered in the same way for an agentic workflow. We need to solve the root of the problem, which is how are these businesses leveraging AI subprocessors. But this quickly becomes an agentic problem that could be far more concerning." For the enterprise privacy and security leaders absorbing this report today, the uncomfortable truth is that the foundational documents and processes they have relied on to manage vendor risk for years are decomposing in real time. The DPA is breaking down as a reliable instrument. State enforcement is accelerating on a bipartisan basis. Privacy teams are shrinking even as their mandates expand. And the next wave of agentic AI systems threatens to distribute unvetted data processing across networks of autonomous agents that operate with even less human oversight than today's tools. Five years ago, when DataGrail published its first trends report, deletion requests were a fraction of what they are today, only a handful of states had privacy laws on the books, and the phrase "shadow AI" did not exist. Every year since, the report has warned that the problem was getting worse. Every year, the data has proved it right. The companies that survive the next chapter will not be the ones with the biggest compliance teams or the thickest policy binders. They will be the ones that accept a disorienting new reality: in 2026, the contracts you signed may not describe the AI that is already processing your customers' data — and by 2027, autonomous agents may be deciding what to do with it.
Canadian firms slow to employ AI agents, survey shows - The Logic
Canadian firms slow to employ AI agents, survey shows - The Logic Canada's Business and Tech Newsroom - Professional Subscription - Partnerships & Advertising - Licensing & Syndication May 27, 2026 | 11:00 AM ET A A A Small A Medium A Large Share Gift Share Some 44 per cent of the executives at large and growing Canadian software companies that sell to other businesses said their companies had adopted agentic AI, according to a survey by investment firm Georgian and research firm NewtonX. Across the U.S., U.K. and Israel, the rate was 67 per cent. (The Logic) Talking point: Canadian businesses are long-standing laggards when it comes to technology adoption, and plenty of studies and reports—including Georgian’s regular surveys—have found the same for AI. The fund’s latest analysis finds Canadian companies are starting to make more sophisticated and widespread use of AI, includi
Google I/O 2026: The Agentic Pivot - FounderCoHo
Google I/O 2026: The Agentic Pivot - FounderCoHo SubscribeSign in # Google I/O 2026: The Agentic Pivot ### How Google is betting its distribution advantage on agents that act — and a multimodal layer that lets them see, hear, and create. May 27, 2026 18 1 Share Upcoming Events: AI-Native Founders: Workflows, Wins & What’s Breaking | Time: Friday, May 29, 6:00 PM - 9:00 PM at Stanford | Register: https://luma.com/founde-wkzr Google’s annual I/O developer conference opened with a keynote that was less a parade of product updates than a declaration of intent. The message was unmistakable: Google believes we have entered the “agentic Gemini era” — a shift in which AI stops being a passive assistant waiting for prompts and becomes a proactive agent that works on the user’s behalf across a redesigned, deeply integrated ecosystem. The agentic push is the headline bet, but it is not th
AI in Security: Infrastructure, Not Hype, Will Determine ROI - Security Industry Association
Too many organizations still treat AI as a collection of use cases instead of a core operational capability—that gap is where value is lost.
The Architecture of Adoption: Why Scalable AI Infrastructure Matters More Than Algorithms for Enterprise Growth
A sophisticated algorithm running on fragmented, rigid architecture is nothing more than an expensive prototype.
Is AI Worth The Cost? Uber And Nvidia Executives Unsure Of Returns On Investments
# Is AI Worth The Cost? Uber And Nvidia Executives Unsure Of Returns On Investments Published: 2026-05-27T11:40:25+05:30 Source: ndtvprofit.com (ndtvprofit.com) Language: en ## Story Is AI Worth The Cost? Uber And Nvidia Executives Unsure Of Returns On Investments Scan to Download Advertisement # Is AI Worth The Cost? Uber And Nvidia Executives Unsure Of Returns On Investments ## Top business leaders feel that it is becoming harder to justify large-scale spending on computing power, given that there is no outlook on near-term returns from artificial intelligence investments. Read Time: 5 mins Share - Twitter - WhatsApp - Facebook - Reddit - Email From Uber's Andrew Macdonald to Nvidia's Bryan Catanzaro, top business leaders have raised concerns over rising costs linked to artificial intelligence. Many believe that it is becoming “harder to justify” the expense of large-scale A
Growing AI costs fuel wider doubts about large-scale automation
Microsoft and Uber are reportedly scaling back their usage of Anthropic's Claude Code tool after the costs became too high.
Research finds 'shadow AI' in vendor software poses growing privacy risk
New research indicates that 64 percent of software vendors fail to disclose additional AI subprocessors in their data processing agreements, creating hidden privacy risks.
Most AI Agents Fail in Production Because They’re Built Backwards | Towards Data Science
Most AI Agents Fail in Production Because They’re Built Backwards | Towards Data Science # Most AI Agents Fail in Production Because They’re Built Backwards Good models don't save bad architecture, and most teams learn that the hard way. May 27, 2026 10 min read Image by author (Generated with ChatGPT) The first time I saw a multi-agent system seriously fail in production, it wasn’t dramatic. There was no crash. No error message. The system just kept running and producing outputs that looked reasonable until someone actually read them carefully enough to notice something was off. When we decided to look into it, it took us two days’ worth of debugging to figure out what was going on. Funny enough, the model wasn’t hallucinating, and the input-output tools were delivering the correct results. The problem, when we finally found it, was architectural. The model and the tools were se
Investors stay calm as AI capex boom eclipses dotcom mania | Reuters
AI ( Artificial Intelligence ) letters are placed on computer motherboard in this illustration taken, June 23, 2023.
AI Coding Startup Cognition Raises $1 Billion at $26 Billion Value - Bloomberg
Cognition AI Inc. has raised more than $1 billion in a new funding round at a $26 billion valuation, the latest sign of strong demand for companies using artificial intelligence for software development.
Samsung memory chip staff in line for £310,000 bonuses after AI profit-sharing deal
Agreement averts strike and shows latest impact of AI boom as two more chipmakers join $1tn club Employees at Samsung Electronics’ memory chip division are to receive bonuses averaging about £310,000 each through a landmark profit-sharing agreement, as the AI boom drives up chipmakers’ profits. Fears of a strike at Samsung were averted on Wednesday after two unions for the world’s largest memory chipmaker said 74% of the 62,616 workers who cast their votes had backed the deal. Continue reading...
Meta Launches AI Chatbot Subscriptions to Boost Revenue Beyond Ads - Bloomberg
Meta Platforms Inc. is selling consumer subscriptions to its Meta AI chatbot for the first time, a key step toward building a business that would help offset hundreds of billions of dollars in artificial intelligence investments by the company.
Snowflake to burn $6B on AWS Graviton CPUs and AI accelerators
Dataware house gambles cloud conveniences, AI accelerated insights will justify the cost.
Cognition\'s 26B valuation shows investors still want autonomous coding agents - Startup Fortune
Cognition has raised more than 1 billion at a 26 billion valuation, underscoring investor belief that autonomous coding agents, not just copilots, are the next
The OpenAI IPO Has Two Masters - by John Polonis
The OpenAI IPO Has Two Masters - by John Polonis # PolisPandit SubscribeSign in # The OpenAI IPO Has Two Masters ### When a non-profit goes public May 27, 2026 ∙ Paid Share Sam Altman, OpenAI CEO, speaking in 2025; Wikimedia Commons Good morning from New York City. Today’s piece returns to the trifecta of AI / Big Tech IPOs we could see this year, with a specific focus on OpenAI and its reported confidential filing to go public. Last week, we discussed SpaceX and how index rules have changed, which will likely admit SpaceX into the S&P 500 and Nasdaq-100 despite its foreseeable future of losing billions annually. Those changes also help OpenAI too. As every fund or ETF that tracks these indexes will become forced buyers of megacap benchmarks with looser standards. It’s like we’re suffering collective amnesia from the spectacular history of Wall Street excesses. For if a trend
Micron, SK Hynix hit $1trn valuation amid AI chip demand
Micron and SK Hynix join rival chipmaker Samsung in the $1trn club, after the latter hit the milestone valuation at the start of the month. Read more: Micron, SK Hynix hit $1trn valuation amid AI chip demand
What Anthropic Becoming Top Private AI Firm Means for Investors | Investing.com
What Anthropic Becoming Top Private AI Firm Means for Investors | Investing.com Micron joins $1 trillion club; Iran talks in focus - what’s moving markets Oil prices sink amid U.S.-Iran negotiations, Hormuz deal hopes Asia stocks: Japan, S.Korea hit records on Wall St tech rally; Iran fears persist Micron stock hits $1T for first time as UBS sees more than 100% upside from here # What Anthropic Becoming Top Private AI Firm Means for Investors Published 05/27/2026, 05:40 AM What Anthropic Becoming Top Private AI Firm Means for Investors View all comments (1)1 Articles(13)| My Homepage Follow Anthropic is closing in on a $900 billion valuation this week. That number is not a typo. According to Bloomberg and the Financial Times, the Claude maker is finalizing a funding round exceeding $30 billion. If it closes as reported, Anthropic will surpass OpenAI as the world’s most valuabl
AI coding startup Cognition raises $1B at $25B pre-money valuation | TechCrunch
As Cognition reaches $492 million in annualized revenue run rate, it more than doubled its valuation in eight months, it says.
AI rally: SK Hynix, Micron Technology join trillion-dollar club; Taiwan bourse overtakes India in market value - The Economic Times
AI rally: SK Hynix, Micron Technology join trillion-dollar club; Taiwan bourse overtakes India in market value - The Economic Times Business News Tech Tech & Internet AI rally: SK Hynix, Micron Technology join trillion-dollar club; Taiwan bourse overtakes India in market value # AI rally: SK Hynix, Micron Technology join trillion-dollar club; Taiwan bourse overtakes India in market value By , ETtechLast Updated: May 28, 2026, 11:39:00 AM IST Share Font Size AbcSmall AbcMedium AbcLarge Save ### Synopsis The AI boom is fueling a surge in chipmakers like SK Hynix, Samsung, and Micron, pushing them into the $1 trillion market-cap club. This global tech rally has propelled Taiwan past India in market value, highlighting the dominance of AI-driven semiconductor giants. Listen to this article in summarized format Listen TIMESOFINDIA.COM The global $1 trillion market-cap club is
AI Factories: The New Infrastructure of Intelligence | NVIDIA Blog
# AI Factories: The New Infrastructure of Intelligence | NVIDIA Blog Author: Jeremy Graybill Published: 2026-05-27T16:00:36+00:00 Source: blogs.nvidia.com (blogs.nvidia.com) Language: en ## Story AI Factories: The New Infrastructure of Intelligence | NVIDIA Blog # AI Factories: The New Infrastructure of Intelligence May 27, 2026 by Your browser does not support the video tag. Share AI factories are a new class of infrastructure built to manufacture intelligence that’s always on and in real time. In the industrial age, power plants converted energy into electricity. In the AI age, AI factories convert energy into tokens — the unit of production for reasoning models, agents and intelligent systems. Their economics are defined by what they produce: tokens per second, tokens per watt, cost per token, utilization and uptime. In this model, performance per watt translates directly int
Another Company Trades AI Layoffs For Stock Price - 24/7 Wall St.
It has become a pattern. A public company lays off employees and says it has found new efficiencies due to AI. Its stock trades higher immediately. This happened yesterday. The job loss count was modest. Groupon (NASDAQ: GRPN) cut 400 people, but the cut was high relative to its overall employee ...
Why TSMC Stock Is Rallying Today: Demand, Guidance, and Outlook – ICO Optics
## Nvidia’s $150 Billion Pledge Propels TSMC to New Heights, Reshaping the AI Semiconductor Landscape This article digs into the […]
Micron Joins $1 Trillion Club After UBS Raises Stock Target on Strong AI Chip Demand
Other chip names also earned profits during the session. Reports said Marvell Technology, Advanced Micro Devices, Intel, Qualcomm, ON Semiconductor, and Lam Research moved higher as investors added exposure to the broader AI hardware supply chain.
SK Hynix hits $1 trillion valuation as AI boom lifts South Korean chip stocks - IndiaVision India News & Information
SK Hynix hits $1 trillion valuation as AI boom lifts South Korean chip stocks IndiaVision India News & Information Shares of SK Hynix soared over 11% on Wednesday, pushing the South Korean memory-chip maker above the $1 trillion market capitalization mark.
DeepSeek Slashes AI Model Prices by 75%, Signaling Intensifying Industry Price War < IT·Gaming < 기사본문 - The Elec Inc.
Some overseas developer communities ... that AI competition is increasingly shifting from building the smartest models to delivering the lowest-cost services. While OpenAI and Anthropic are maintaining premium pricing strategies backed by performance, DeepSeek's cost competitiveness presents a performance-to-price ratio that ...
India’s Data Centre Sector Eyes USD 23 Billion AI Opportunity
India could deploy up to 7 lakh GPUs in data centres over five years, creating a USD 23 billion opportunity amid rising AI adoption, says Avendus
AI Data Center Demand Pushes Global DRAM Revenue to Record High in Q1 2026 - InfotechLead
The DRAM industry delivered record-breaking growth in Q1 2026 as soaring artificial intelligence infrastructure demand and memory prices
Buy or Sell Amazon Stock in 2026? AWS AI Growth Fuels Debate on Premium Valuation
Some analysts have recently adopted a more neutral stance, recommending investors wait for pullbacks before initiating new positions. They highlight that much of the positive AI narrative may already be priced in, particularly given the stock's strong performance throughout 2026.
Buy or Sell Meta Stock in 2026? Analysts Weigh AI Strength Against Valuation Concerns
NEW YORK — As Meta Platforms ... in late May 2026, investors face a familiar dilemma: whether to buy into the company's artificial intelligence momentum and advertising dominance or exercise caution over elevated valuations and ongoing losses in its Reality Labs division. The social media giant has delivered strong returns year-to-date, driven by robust ad revenue growth and progress in its open-source AI ...
Council Post: Why Domain-Specific AI Is Reshaping Enterprise Strategy
For CISOs evaluating AI, the key question is whether a model truly understands the environment it operates in.
The New AI Corridor: How the US, Europe, and Middle East Are Rewiring the Future of Digital Infrastructure | Morgan Lewis - Data Center Bytes - JDSupra
For decades, global infrastructure strategy revolved around oil pipelines, shipping lanes, and manufacturing hubs. Today, a new geopolitical network built on fiber networks, power grids,...
China expands travel curbs to top AI talent at private firms - The Japan Times
China expands travel curbs to top AI talent at private firms - The Japan Times Subscribe - Iran war endgame - Philippines-Japan summit - “Tokuryu” crime - Latest News Subscribe for more access # China expands travel curbs to top AI talent at private firms People visit an Alibaba booth during the World Artificial Intelligence Conference in Shanghai on July 26, 2025. | REUTERS China is restricting overseas travel for top AI professionals in private firms such as Alibaba Group and DeepSeek, suggesting an escalation in measures intended to safeguard its technology and catch up to the U.S. in a pivotal sphere. Government agencies have begun imposing restrictions on individuals involved in advanced AI work and considered strategically important to the country, people familiar with the matter said. That means they need approval from relevant authorities before embarking on overseas trave
Armenia Activates Supercomputer, Vying for Global AI Hub Status - BriefGlance.com
With a new $120M AI factory powered by NVIDIA's latest chips, Armenia is making a bold play for technological sovereignty and a spot on the global AI map.
Micron and SK Hynix Join Trillion-Dollar Club as AI Memory Demand Surges | AlphaPilot
Geopolitical tensions and export controls on high-end AI hardware to China remain a persistent regulatory risk.
Agent Control Standard Launches Open Framework for Runtime Governance of AI Agents
The Agent Control Standard (ACS) today announced a vendor-agnostic, open standard for governing AI agents at runtime.
AI & Tech Brief: The techno-optimist’s case
# AI & Tech Brief: The techno-optimist’s case Published: 2026-05-27T19:00:36.561000+00:00 Source: washingtonpost.com (washingtonpost.com) Language: en ## Story I interviewed David George, general partner at Andreessen Horowitz, regarding narratives around AI jobs displacement and the “permanent underclass.” George argues that the AI revolution will augment workers, not substitute them.
Champion ethical hacker warns AI tools like Mythos will make competing harder
Chompie, one of the world's tops ethical hackers, says AI like Claude Mythos will make it harder for people like her to compete.
Tech Leaders Rethink AI’s Impact On Jobs As Layoffs Shift Debate From Replacement To Collaboration - The CSR Journal
Tech Leaders Rethink AI’s Impact On Jobs As Layoffs Shift Debate From Replacement To Collaboration - The CSR Journal Sign in Sign in Welcome!Log into your account your username your password Forgot your password? Password recovery Recover your password your email Search Search May 28, 2026 # Tech Leaders Rethink AI’s Impact On Jobs As Layoffs Shift Debate From Replacement To Collaboration Published By - Aakanksha Yadav May 27, 2026 Reading time - 3 min. #### The narrative surrounding artificial intelligence (AI) and its impact on the future workforce is undergoing significant changes, especially after 2.5 lakh individuals lost their jobs in recent months. Initially, tech leaders, including Sam Altman of OpenAI and Dario Amodei of Anthropic, painted a picture of a bleak job market where AI would replace human workers en masse. Their warnings, once echoed in multiple forum
Competitive Business Leaders Need Clear AI Vision to Break the Ceiling of Innovation - SPONSOR CONTENT FROM IBM
AI’s impact on organizational culture is already visible. Two-thirds of responding employees say AI is changing their company culture, according to 5 Trends for 2026, and 88% of them describe that shift as positive. Nearly half say they would even be open to AI agents managing them.
In 2026, how might engineers ‘get noticed’ by large tech organisations?
SiliconRepublic.com spoke with experts from Yahoo Mail about standing out in a competitive field and the opportunities open to jobseekers. Read more: In 2026, how might engineers ‘get noticed’ by large tech organisations?
Why Sundar Pichai Says Public is Right to be Concerned of AI | AI Magazine
Why Sundar Pichai Says Public is Right to be Concerned of AI | AI Magazine Article AI Strategy # Why Sundar Pichai Says Public is Right to be Concerned of AI May 27, 2026 undefined mins Share Share Google CEO Sundar Pichai says while young people are right to feel anxious about AI, they should understand they play a key role in the shaping of the technology (Credit: Getty) Google CEO, Sundar Pichai says that young people are right to feel concerned about AI, while defending long term benefits of the "most profound technology" The AI led layoffs have given the technology and its advocates troublesome public encounters – most notable was the booing of former Google CEO Eric Schmidt by University of Arizona students after the executive praised AI’s potential. Sundar Pichai, Google's current CEO has since acknowledged that public anxiety around AI is justified. His comments come a
Why Memory Chips Are Dominating the A.I. Rally - The New York Times
Three major producers — Micron, Samsung and SK Hynix — are now trillion-dollar companies. Politicians and Wall Street have taken notice.
MiniMax teases upcoming M3 model with new sparse attention mechanism and 15.6X long-context response speed boost
Among the many Chinese AI companies and laboratories vying for market share and attention (no pun intended) on the global marketplace, MiniMax stands out for its commitment to providing frontier-level intelligence across a range of modalities, including text, coding, and video (through its Hailuo model series) — often under permissive, enterprise-friendly, standard open source licenses. Now, MiniMax is again raising the eyebrows of AI power users and developers around the world by releasing a new, in-depth technical report on the making of its popular M2 series of language models (M2, M2.5, and M2.7) shedding light on its numerous engineering innovations and clever approaches — while the company and its leaders also teased a whole new sparse attention approach for its upcoming MiniMax M3 series of models, which it says yields up to 15.6 times faster decoding (or LLM response) speed at long contexts (a million tokens) by adopting a custom sub-quadratic framework. In so doing, MiniMax has designed M3 to make ultra-long-context AI agent deployment economically viable. The M2 report is noteworthy for any enterprise working with AI models, and especially those looking to fine-tune and train their own in-house. After all, MiniMax's M2 series models often achieved top benchmarks in the world for open source AI performance when they were released. While the title has since been eclipsed by several other Chinese labs including DeepSeek and Xiaomi, MiniMax's new report offers a blueprint that can be used to improve AI model and agent performance by enterprises around the world. As Adina Yakup of Hugging Face observed on X, "Beyond the benchmarks, they’ve done some really solid work on MoE efficiency and agent oriented design. Excited to see where M3 goes next!" The attention dilemma The core technical architecture of the M2 series relies on a sparse Mixture-of-Experts (MoE) decoder-only Transformer layout used by numerous other state-of-the-art LLMs. The foundational backbone houses 229.9 billion total parameters, yet maintains a remarkably lean operational footprint by activating just 9.8 billion parameters per token across 256 fine-grained experts. To optimize routing and avoid standard load-balancing issues, however, MiniMax implemented sigmoid gating paired with learnable, expert-specific bias terms, heavily reducing reliance on restrictive auxiliary losses. The most definitive engineering decision documented in the M2 paper was the strict adherence to full multi-head attention with Grouped Query Attention (GQA) across all 62 layers. In large language models, "quadratic scaling" refers to the computationally expensive reality of standard full attention mechanisms, where every token in a sequence must mathematically connect to every other token. To use a real-world analogy, it is akin to attending a networking event and being forced to have a deep conversation with every single person in the room while simultaneously monitoring all other ongoing conversations. While this approach yields incredibly thorough context, the processing power and memory required explode at the square of the input length, creating a severe hardware bottleneck as models attempt to ingest hundreds of thousands of words. The problem with sub-quadratic scaling "Sub-quadratic" scaling introduces architectural shortcuts designed to bypass this exponential computational load. Instead of mapping every possible connection, sub-quadratic methods—such as Sliding Window Attention or compressed linear attention—might only analyze a localized window of nearby words or generate a compressed summary of the broader text. These efficient methods drastically reduce hardware costs and allow models to process massive documents at high speeds, but they historically introduce severe trade-offs in accuracy, often causing the AI to miss the "big picture" or lose track of distant context. This mathematical dilemma defines the architectural evolution from MiniMax's M2 to its upcoming M3 series. During M2's development, researchers rigorously tested sub-quadratic shortcuts but found they crippled the model's "multi-hop reasoning"—its ability to connect disparate clues across a long document—forcing the team to absorb the massive computational cost of full quadratic attention to maintain frontier-level intelligence. Indeed, they aggressively benchmarked efficient attention alternatives during pre-training but intentionally threw them out. They experimented extensively with hybrid setups, interleaving full attention with sub-quadratic architectures like Lightning Attention or hybrid Sliding Window Attention (SWA) configurations. The empirical results were definitive: at a larger scale, linear and windowed attention variants exhibited severe reasoning deficits. On evaluations exceeding 32K context windows, SWA variants performed significantly worse than full attention, dropping from a baseline score of 90.0 to 72.0 on the RULER 128K complex word extraction task. Sub-quadratic configurations proved prone to memory-bound constraints during training, lacked native prefix caching support, and failed to smoothly align with Multi-Token Prediction (MTP) modules used for speculative decoding. Full attention was deemed necessary to preserve multi-hop reasoning capability. However, recognizing that physical hardware limits cannot sustain quadratic scaling indefinitely, MiniMax is designing the M3 series around a novel sub-quadratic framework to finally deliver both high-speed processing and uncompromised reasoning. MiniMax Sparse Attention (MSA) and sub-quadratic scaling incoming The upcoming MiniMax-M3 breaks away from the compute-heavy constraints of its predecessor. As disclosed by MiniMax’s engineering team under the banner "Something BIG is coming," M3 introduces "MiniMax Sparse Attention" (MSA). Unlike DeepSeek’s Multi-head Latent Attention (MLA), which compresses keys and values into a low-dimensional latent space, MSA operates on a standard GQA backbone but utilizes block-level selection on real, uncompressed Key-Values. Elie Bakouch at AI training infrastructure and platform lab Prime Intellect posted on X noting that the main changes feature "block level selection like in CSA but attention is done on the real KV, not in [compressed space]." This solves the precision loss and prefix-caching obstacles noted in the M2 paper. By filtering and selecting block-level sequences dynamically, MSA delivers an architectural leap: early hardware profiling indicates a 9.7x speedup in prefilling latency and a massive 15.6x speedup during decoding phases at a 1-million token sequence length compared to the full-attention M2 architecture. To understand why a speedup in the "decoding phase" is so significant, it helps to break down how an AI actually reads and writes information. When you interact with an AI, the processing happens in two distinct steps: prefilling and decoding. When you hand an AI a prompt—whether it’s a short sentence or a massive 1,000-page document—it processes that entire chunk of text all at once in parallel, known as "prefilling." It essentially "reads" the input in one big gulp to build its initial understanding and establish context. In order to generate a response, the AI must enter a "decoding phase." To predict the first word of its response, it looks at the prompt. To predict the second word, it has to look at the prompt plus the first word. To predict the hundredth word, it must recalculate the context of the prompt and the previous 99 words it just wrote. So the response actually becomes harder to generate as it goes on, with the end requiring a full review of all prior parts. For a layperson, imagine reading a dense legal brief (prefilling) and then being forced to write a summary report where, before writing every single new word, you must rapidly reread the entire brief plus everything you've written so far to ensure your next word makes sense (decoding). Because the AI must constantly and repetitively look backward to generate each new step forward, the decoding phase is the most severe computational bottleneck in generating text. It is why AI models often type out their answers word-by-word, and why they slow down significantly as conversations get longer. Therefore, when the passage states the new architecture achieves a massive 15.6x speedup during the decoding phase at a 1-million token sequence length, it means the model has found a structural shortcut to generate its answer—token by token—nearly 16 times faster. It directly solves the exact bottleneck that normally makes AI chatbots freeze or stutter when handling massive amounts of information. The evolution of the MiniMax M series and the creation of 'Forge' On a product level, MiniMax has consistently evolved its models from simple text generation interfaces into autonomous workers. The M2 series pioneered an "interleaved thinking" protocol where the model alternates between natural-language planning traces and explicit tool invocations inside a single trajectory. Rather than dropping the intermediate chain-of-thought blocks between execution turns, M2 appends the full thinking history directly into the conversation context. This planning persistence prevents state drift, allowing the model to recover gracefully from runtime errors and revise its strategies based on environment feedback. To train these long-horizon workflows, MiniMax built "Forge," a scalable agent-native reinforcement learning system. Forge decouples execution into three independent modules—the Agent Side, the middleware abstraction layer (Gateway Server and Data Pool), and the Training/Inference engines. As MiniMax engineer Olive Song explained on the ThursdAI podcast, "What we realized is that there's a lot of potential with a small model like this if we train reinforcement learning on it with a large amount of environments and agents... But it's not a very easy thing to do," adding that this environmental training was where the team spent a significant portion of their development timeline. To absorb the extreme trajectory-length variance common in multi-step agent environments, Forge implements two vital engineering solutions: Windowed FIFO Scheduling: A training scheduler that maps a sliding window over the generation queue. It permits greedy, high-throughput fetching of completed tasks within the window to prevent cluster idle time, while strictly enforcing FIFO boundaries to maintain distributional stability and avoid gradient oscillation. Prefix Tree Merging: An optimization that restructures batch training into tree computation. Completions sharing identical conversation prefixes are calculated exactly once in the forward pass before branching. This eliminates redundant calculations, generating up to a 40x training speedup with zero approximation error. This reinforcement infrastructure directly spawned the M2.7 checkpoint, moving the series toward "self-evolution". Operating inside an automated agent harness, M2.7 functions as an independent machine learning engineer. The model profiles its own active training runs, diagnoses anomalies, reads logs, and automatically modifies its own codebase and configurations. According to MiniMax, M2.7 successfully handled between 30% and 50% of its own development workflow. On OpenAI’s rigorous MLE Bench Lite suite, which tests autonomous ML research capability, M2.7 achieved a 66.6% medal rate across independent 24-hour trials, effectively tying Google’s closed-weight Gemini 3.1 Pro. The continuous cadence from M2 to M2.5, which famously completed 30% of internal tasks and 80% of newly committed code at MiniMax HQ, underlines a broader vision. As the MiniMax team noted during that phase of deployment, "we believe that M2.5 provides virtually limitless possibilities for the development and operation of agents in the economy." With the technical report codifying the M2 generation's successes and the MSA tech blog on the horizon, MiniMax is signaling that the next frontier of AI is explicitly about translating a mini-activation footprint into maximum real-world intelligence.
Micron Joins $1 Trillion Club as AI Memory Demand Soars
According to one estimate, nearly 20% of the world’s semiconductor design engineers are based out of Indian tech hubs like Bengaluru and Hyderabad, making India indispensable to the R&D pipelines of companies designing next-generation AI hardware. ... As the AI revolution demands tighter integration between processors and memory, the global supply chain ...
From Search Engines to Autonomous Agents: AI industry enters its next phase - The Economic Times
The AI industry is rapidly shifting ... AI” systems capable of independently executing complex tasks across workflows and applications. Companies including OpenAI, Microsoft, Google, and Salesforce are leading investments in AI agents designed to automate research, customer support, scheduling, and enterprise operations. The shift is expected to redefine productivity, workplace automation, ...
Welcome to May 26, 2026 - by Dr. Alex Wissner-Gross
Welcome to May 26, 2026 - by Dr. Alex Wissner-Gross # The Innermost Loop SubscribeSign in # Welcome to May 26, 2026 May 27, 2026 55 8 1 Share Article voiceover 0:00 -6:44 Audio playback is not supported on your browser. Please upgrade. The Singularity is now taking a power nap. CMU researchers propose in“Language Models Need Sleep” that models should periodically consolidate recent context into persistent fast weights inside SSM blocks before clearing the cache, with longer sleep yielding the largest gains on examples that demand deeper reasoning. Waking hours are doing recursive work too. The new“BenchBench” benchmark asks whether a model can write a benchmark that other strong models cannot simply clear, and GPT-5.2 currently leads as the top benchmark creator. Microsoft pushes the same recursion into agents with SkillOpt, which treats a compact natural-language skill docu
Datacentre dive: AI factory power draw changes the grid calculus | Computer Weekly
But water use will likely decrease. We look at energy as the key driver – and bottleneck – in development and why water use is less of an issue now datacentres aren’t like a VW Beetle
OpenAI launches Korea cyber action plan with expanded AI security access
OpenAI is expanding access to its Trusted Access for Cyber program for South Korean government agencies and companies to bolster cybersecurity capabilities.
NVIDIA Vera Rubin and the Future of Agentic AI Infrastructure
NVIDIA Vera Rubin are gaining attention as the foundation for the next wave of AI innovation
Big Tech extracts retirement-scale wealth from UK internet users, research shows
Britain's 'free' internet economy is powered by invisible data extraction that feeds advertisers, AI firms, and digital platforms.
ISM 1.0 gave India a seat at the chip table. ISM 2.0 will decide if it stays there - BusinessToday
Chandak added that the next phase must also strengthen India’s design-led semiconductor ecosystem through expansion of DLI support, fabless semiconductor startups, indigenous IP creation, AI hardware innovation and product engineering.
Netherlands blocks US takeover of vital digital supplier
The Dutch government has intervened to block the acquisition of a critical digital infrastructure supplier by a US firm.
Google, Singapore to ramp up National AI Strategy | Frontier Enterprise
Google and the Ministry of Digital Development and Information (MDDI) are collaborating to harness frontier AI as a force for good – including deploying AI to solve society’s challenges, fostering an AI-ready workforce in Singapore, driving enterprise innovation, and creating a secure ecosystem.
Data in motion will enable the agentic enterprise: Boomi CEO | Frontier Enterprise
# Data in motion will enable the agentic enterprise: Boomi CEO | Frontier Enterprise Author: Anthony Macarayan Published: 2026-05-27T01:00:00+00:00 Source: frontier-enterprise.com (frontier-enterprise.com) Language: en ## Story Data in motion will enable the agentic enterprise: Boomi CEO | Frontier Enterprise Search Sign in Welcome! Log into your account your username your password Forgot your password? Get help Password recovery Recover your password your email A password will be e-mailed to you. Home Frontier Tech AI & ML Data in motion will enable the agentic enterprise: Boomi CEO Share Boomi CEO Steve Lucas opened his keynote with a declaration that data is not the new oil. Boomi is arguing that the biggest obstacle to enterprise AI adoption isn’t the models themselves, but whether enterprise data is actually usable in real time. At Boomi World 2026 in Chicago, the c
Council Post: Agentic AI Won’t Scale Without Enterprise Context
Context is what makes agentic solutions perform better, think better, take actions and repeat actions—and do so in a uniform way.
Anthropic Growth and Bedrock Mix Drive AWS Margins Higher While Peers Lag
Anthropic Growth and Bedrock Mix Drive AWS Margins Higher While Peers Lag SubscribeSign in # Anthropic Growth and Bedrock Mix Drive AWS Margins Higher While Peers Lag ### Amazon’s Bedrock Mix and Anthropic Deal Terms Combine to Show Greater Operating Leverage Jeremie Eliahou Ontiveros, Joey Brookhart, Crystal Huang, and Dylan Patel May 27, 2026 ∙ Paid 44 2 Share While other CSPs have seen declinin
In AI Infrastructure, the Offtake Agreement Is the Asset
Committed Demand as the Primary Credit Variable, Counterparty Quality Over Real Estate, Compute Factory Underwriting from First Principles, What CoreWeave's Microsoft Contract Actually Is
CSP capex surge fuels hot AI server demand and tight supply in 2026
The global AI server market continues to expand at a rapid pace, driven by higher capex from large cloud service providers (CSPs) and rising demand for generative AI infrastructure. As a result, the broader server industry is set to post another strong year in 2026, while the AI server supply ...
Venture Capital & Startup Funding Roundup, May 26, 2026 - Tech Startups
It’s Tuesday, May 26, 2026, and welcome to another edition of the Venture Capital & Startup Funding Roundup. The biggest story in today’s funding tape is not that investors are still pouring money into AI. It’s where that capital is suddenly concentrating.
DeepSWE blows up the AI coding leaderboard
DeepSWE has disrupted the AI coding leaderboard, crowning GPT-5.5 as a top performer while identifying a loophole in Claude Opus's benchmark performance.
Gambit Says Speed of AI-Powered Cyberattacks Drives Need for Cyber Resilience | PYMNTS.com
Recent cyberattacks by a persona linked to Iran reportedly demonstrate the speed with which AI-enhanced attacks can be carried out.
Cybersecurity Roundup: Partnerships, Funding, and Emerging Threats — May 26, 2026 | CISA, Securonix, GRAMAX Cybertech, TELUS Digital, Upstream Security, and Autonomous Patching
Home » Blog » Cybersecurity Roundup: Partnerships, Funding, and Emerging Threats — May 26, 2026 | CISA, Securonix, GRAMAX Cybertech, TELUS Digital, Upstream Security, and Autonomous Patching ... The latest headlines point in one direction: AI is speeding up both attacks and defenses, critical-infrastructure operators are relying more on managed cyber defense partnerships, enterprise buyers are demanding clearer AI safety ...
The LLM Doesn't Matter Anymore
The LLM Doesn't Matter Anymore # From The Ground Up with Max Mitcham SubscribeSign in # The LLM Doesn't Matter Anymore ### The 2026 fight isn't which model is smartest. It's the infrastructure underneath it. May 27, 2026 Share Every founder I talk to is still picking a side in the wrong war. GPT vs Claude vs Gemini. Whoever shipped the smartest model this week. I think that’s already over. Not because the models stopped mattering, but because the gap between them stopped mattering. Pick any frontier LLM in 2026 and it will perform incredibly well if you wrap the right infrastructure around it. Pick the smartest model on earth and feed it nothing about your business or your market, and it will still produce slop. The actual fight is two layers underneath the model: the context engine you build to feed the agent, and the memory system you scaffold around that context. Skills sit o
The Shift: Code, Context & Conversational Search
The Shift: Code, Context & Conversational Search SubscribeSign in # The Shift: Code, Context & Conversational Search ### The era of "more is better"—whether that means bloating your codebase, drowning search engines in programmatic AI content, or treating LLMs as simple text boxes—is officially over. SWIPE and Nebojsa Radakovic May 27, 2026 2 Share I’m not sure yet what to make of what has happened over the last two weeks, but… it looks like a hyper-focused race toward lean, multi-agent orchestration and high-authority "evidence layers" that prioritize deep, systemic value over cheap volume. Devs are shifting toward optimizing velocity and systemic security by balancing the use of AI tools with strict controls against vulnerabilities. This trend also includes a practical pushback against architectural bloat, favoring lightweight stacks and unified engines to build fast, secure s