AI News Archive: July 10, 2026 — Part 4
Sourced from 500+ daily AI sources, scored by relevance.
- How knowledge-based AI helps autonomous vehicles follow traffic laws
How knowledge-based AI helps autonomous vehicles follow traffic laws Automotive News
Score: 65🌐 MovesJul 10, 2026https://www.autonews.com/technology/ane-knowledge-based-ai-forvia-mercedes-0618/ - From steel to smart steel: Re-engineering manufacturing for the AI era
From steel to smart steel: Re-engineering manufacturing for the AI era Techcircle
Score: 65🌐 MovesJul 10, 2026https://www.techcircle.in/2026/07/10/from-steel-to-smart-steel-re-engineering-manufacturing-for-the-ai-era/ - The AI power law: Why 2% of AI agents create most enterprise value
The organisations that will lead in AI will be those that identify their power law use cases fastest − and scale them before their competitors do.
- ‘The challenge now is making sure the next generation develops those same foundations before relying too heavily on AI’: Devs are swerving fundamental skills like Git and Agile because of AI – but there’s a good reason
‘The challenge now is making sure the next generation develops those same foundations before relying too heavily on AI’: Devs are swerving fundamental skills like Git and Agile because of AI – but there’s a good reason IT Pro
- Cisco brings enterprise AI infrastructure to the edge: CUBEd Award winner
Artificial intelligence is changing the technology equation in numerous ways, including how enterprises approach edge computing. Much of today’s edge infrastructure was not built to support AI workloads. Cisco Systems Inc. is seeking to close that gap with Unified Edge, which earned the company the 2026 Tech Innovation CUBEd Award for the most innovative IoT […] The post Cisco brings enterprise AI infrastructure to the edge: CUBEd Award winner appeared first on SiliconANGLE .
Score: 65🌐 MovesJul 10, 2026https://siliconangle.com/2026/07/10/cisco-edge-computing-hardware-platform-ai-thecube-ciscoaward/ - AI could affect millions in South-east Asia as ILO sees no big job cuts
AI could affect millions in South-east Asia as ILO sees no big job cuts The Straits Times
- SK, SoftBank partner to develop AI social value standard
SK, SoftBank partner to develop AI social value standard 매일경제
- LG CNS partners with UK's PhysicsX to develop industrial AI model
LG CNS has signed a partnership agreement with UK-based physical AI startup PhysicsX to jointly develop next-generation industrial artificial intelligence models, according to industry sources Thursday. PhysicsX, which has received investment from Nvidia and is valued at $2.4 billion, develops industrial AI models that improve engineering and manufacturing processes for clients in industries including aerospace and automotive. PhysicsX recently announced the collaboration on its LinkedIn account
- Atlassian Rovo agents: What we learned building an AI assistant for Jira
Atlassian Rovo agents: What we learned building an AI assistant for Jira Atlassian Community
- Amazon and University of Michigan give robots a sense of touch
HydroShear, a new physics-based simulator, teaches robots how to use their sense of touch to perform complex manipulation tasks, in a way that transfers seamlessly to the real world.
Score: 65🌐 MovesJul 10, 2026https://www.amazon.science/blog/amazon-and-university-of-michigan-give-robots-a-sense-of-touch - Chinese Driverless Mining Truck Specialist Surges on Hong Kong Debut
Chinese Driverless Mining Truck Specialist Surges on Hong Kong Debut Caixin Global
- Google expands AI infrastructure in India with local Gemini hosting
Google expands AI infrastructure in India with local Gemini hosting YourStory.com
Score: 65🌐 MovesJul 10, 2026https://yourstory.com/ai-story/google-cloud-gemini-models-hosted-in-india - Sam Altman said everyone at Sun Valley wants to know how to make AI cheaper
Sam Altman said everyone at Sun Valley wants to know how to make AI cheaper Business Insider
Score: 65🌐 MovesJul 10, 2026https://www.businessinsider.com/sam-altman-sun-valley-conference-cut-ai-costs-2026-7 - The Work of Helping A.I. Destroy Work
Start-ups are paying white-collar professionals to teach their jobs to artificial intelligence models. It’s a bonanza. It’s bleak. Where will it end?
- Researchers build missing infrastructure to move AI between robots
Robotics researchers often spend weeks, or even months, simply getting a new robot up and running before they can begin testing new behaviors. Researchers in the Carnegie Mellon University School of Computer Science have developed an open-source software framework designed to eliminate much of that setup work, making it easier to deploy AI systems across different robots without rebuilding software from scratch.
- Meta's New AI Tool Creates Deepfakes. Here's How to Protect Yourself on Instagram
All Instagram users, public or private, can change their settings now to opt out.
Score: 65🌐 MovesJul 10, 2026https://www.cnet.com/tech/services-and-software/meta-muse-ai-image-content-reuse-deepfake-news/ - Relearning cloud lessons from runaway AI token costs
Relearning cloud lessons from runaway AI token costs InfoWorld
Score: 65🌐 MovesJul 10, 2026https://www.infoworld.com/article/4194092/relearning-cloud-lessons-from-runaway-ai-token-costs.html - Google's TabFM skips per-dataset training and still predicts on tables it's never seen
The vast majority of business data is tabular — living in data warehouses, CRMs, and financial ledgers — yet building a reliable model from it still means training a new one from scratch for every dataset, then maintaining hyperparameter tuning loops, feature engineering, and retraining pipelines to fight data drift. Google Research is proposing a way around that: a new foundation model called TabFM that treats tabular prediction as an in-context learning problem instead. It can generate predictions for a new, unseen table in a single forward pass. For enterprise developers and AI engineers, this reduces the time-to-production from weeks of pipeline engineering to a single API call. The challenge with traditional ML To extract reliable predictions from a gradient-boosted tree, data scientists must build and maintain complex data pipelines. They have to clean messy inputs, impute missing values, encode categorical variables into numerical formats, and engineer custom feature crosses. Once the data is ready, they must run repetitive hyperparameter optimization loops, searching across learning rates, tree depths, subsampling ratios, and regularization grids to find the best configuration. Once deployed, these traditional models "incur ongoing operational debt through data drift monitoring and retraining pipelines to stay accurate," Weihao Kong, Research Scientist at Google Research, told VentureBeat. Meanwhile, the rest of the AI industry has moved on. Generative AI models for text and computer vision have seamlessly shifted to zero-shot inference, where a model can perform a completely new task simply by being prompted with context. Large language models (LLMs) already excel at in-context learning , so why can't we just feed tables into an off-the-shelf LLM? Because LLMs are trained on natural language rather than structured data, they struggle to process tables directly. First, their context limits are exhausted quickly by medium-sized tables containing just a few thousand rows and hundreds of columns. Second, LLMs suffer from tokenization inefficiency, awkwardly splitting numerical values and destroying mathematical precision. Finally, they suffer from structural blindness. When a 2D table is serialized as a 1D text string, LLMs lose track of which value belongs to which row and column as the table grows. "That's why, today, it is far more effective to use an LLM to write the code that handles feature engineering and calls XGBoost than to ask the LLM to read the table itself," Kong said. What is TabFM? To run inference with TabFM, you do not update any model weights. Instead, you take your historical examples (the training rows with their known labels) and your target rows (the new data you want to predict) and pass them to the model as a single, unified prompt. The model learns to interpret the relationships between columns and rows directly from this context at runtime. For example, consider an enterprise analyst trying to predict customer churn. Instead of building a bespoke data pipeline and training an XGBoost model, they can simply pass a sample of historical user session data alongside a new, active session into TabFM. In one forward pass, the model returns an instant churn probability. TabFM overcomes the limitations of LLMs by treating the data as a grid, preserving its structural integrity without forcing it into a single-dimensional text string. To effectively process diverse tabular structures while enabling scalable zero-shot prediction, TabFM synthesizes the strengths of earlier experimental architectures, TabPFN and TabICL. TabPFN , developed by Prior Labs, first proved that a transformer architecture could perform zero-shot classification on small tables, though it struggled to scale computationally to larger datasets. Later, TabICL , developed by France's National Research Institute for Digital Science and Technology, addressed this bottleneck by introducing row compression, allowing in-context learning to efficiently process much larger tables. TabFM combines TabPFN's deep feature contextualization with TabICL's efficient compression into a novel hybrid design built on three key mechanisms: 1. Alternating row and column attention: The raw table is first processed through a multilayer attention module that alternates across both columns (features) and rows (examples). By continuously attending across these two dimensions, the model natively captures complex feature interactions. This deep contextualization does the heavy lifting that would usually require tedious manual feature crafting by data scientists. 2. Row compression: Following this contextualization, the cross-attended information for each row is compressed into a single, dense vector representation. TabICL pioneered this by using CLS tokens to compress a row's rich information into one vector, "in contrast to TabPFN v2, v2.5, and v2.6, which attend over the full cell grid throughout the network," Kong explained. This drastically shrinks the computational footprint. 3. In-context learning (ICL): A causal Transformer then operates on this sequence of compressed embeddings. This Transformer model uses the attention mechanism of TabICL to attend over these dense row vectors, drastically reducing the computation cost and allowing the model to process large datasets efficiently. A major selling point of TabFM is its pretraining recipe. The model was trained entirely on hundreds of millions of synthetic datasets. These datasets were dynamically generated using structural causal models (SCMs) that incorporate a wide variety of random functions. By training exclusively on synthetic SCMs, TabFM learned the fundamental mathematical priors of how tabular features interact without ingesting real-world, confidential CSV files. TabFM in action To test the model's capabilities, Google researchers benchmarked TabFM on TabArena, a comprehensive evaluation suite spanning 51 diverse tabular datasets across 38 classification and 13 regression tasks. On these public benchmarks, TabFM's zero-shot predictions already match or beat heavily tuned supervised baselines. However, Google is careful to note that this does not automatically mean TabFM will universally dethrone bespoke, hyper-optimized production models on every enterprise workload. "Instead of replacing hyper-optimized production models, the true practical business value it unlocks for lean engineering teams is velocity," Kong said. "It allows data analysts and backend engineers to instantly spin up high-quality baseline models without a dedicated data science team managing a complex lifecycle." For advanced practitioners looking to squeeze out maximum accuracy, the research team also introduced a "TabFM-Ensemble" configuration. By running the model through 32 distinct variations and blending the results, TabFM pushes the performance even further. Getting started, trade-offs, and the cloud future The shift to in-context learning for tables introduces a new economic trade-off that engineering teams must consider. With traditional algorithms, training is slow and expensive, but inference is lightning-fast and cheap. TabFM flips this dynamic. While training time drops to zero, inference becomes significantly heavier. Because the model must process the entire historical dataset as context during every single prediction, it requires more compute and memory at runtime. In this new paradigm, "traditional machine learning training becomes the 'prefill' phase (KV caching) in the context window," Kong said. While this prefill cost is steep, it is paid only once per table, and the cache is reused across subsequent queries. "The catch is prediction latency, which no amount of caching removes," Kong added. Every new prediction requires a pass through a large transformer. "Any production API requiring single-digit-millisecond response times cannot tolerate TabFM's forward-pass overhead." For developers looking to evaluate the model today, the barrier to entry is low. Google designed TabFM as a drop-in replacement for traditional ML workflows, offering a scikit-learn compatible API (TabFMClassifier and TabFMRegressor). It natively handles mixed numerical and categorical columns, works directly with pandas DataFrames, and requires no manual ordinal encoders or numerical scalers. The library supports both JAX and PyTorch backends. However, enterprise teams need to be aware of current limitations and licensing restrictions. The model architecture has a hard limit of 10 output classes for classification tasks, and it is optimized for tables with up to 500 features. More importantly, while Google released the underlying codebase under the permissive Apache 2.0 license, the pre-trained model weights are published on Hugging Face under a strict tabfm-non-commercial-v1.0 license. Developers can evaluate the model internally, but it cannot be deployed in commercial products yet. Looking ahead, Google is addressing the commercial deployment friction through its cloud ecosystem. TabFM is being integrated directly into Google BigQuery, allowing analysts to run zero-shot predictions natively via an “AI.PREDICT” command. By putting foundation model inference right next to the data warehouse, TabFM could soon make complex tabular machine learning as accessible as a basic database query. In practice, TabFM shines in rapid prototyping, high data drift environments, and small to medium-sized datasets under 100,000 rows. Conversely, teams should stick to traditional models for strict, ultra-low latency APIs, or massive tables exceeding one million rows, which currently require aggressive row sampling that degrades the foundation model's competitive advantage.
- SK Hynix rises nearly 13% in debut on Wall Street as demand for memory chips soars amid AI frenzy
SK Hynix rises nearly 13% in debut on Wall Street as demand for memory chips soars amid AI frenzy San Francisco Chronicle
Score: 65💰 MoneyJul 10, 2026https://www.sfchronicle.com/business/article/sk-hynix-hits-the-u-s-stock-market-as-demand-for-22340106.php - How GPT-5.6 Reflects the New AI Regulation
The release shows the power the U.S. government now holds in the AI model landscape. ChatGPT Work highlights how OpenAI continues to evolve into an enterprise vendor.
Score: 64🌐 MovesJul 10, 2026https://aibusiness.com/generative-ai/how-gpt-5-6-reflects-new-ai-regulation - 57% of enterprises have watched AI agents be confidently wrong. The fix is an agentic context layer, but who has one?
An enterprise AI agent answers with total confidence, but the number is wrong. Nobody catches it until someone traces it back to a stale metric definition or a document the retrieval system never pulled. The model did not fail. The context it was given did. In the past six months, 57% of enterprises traced a confident but wrong AI agent answer to missing or inconsistent business context, and 31% said it happened more than once, according to a VB Pulse June 2026 survey of 101 qualified enterprises with more than 100 employees. The reason is not hard to find. Retrieval over documents is the default way agents get business context for 38% of enterprises, nearly double the next closest approach. The way most enterprises choose a retrieval system compounds the problem. Ease of ingestion and operational simplicity lead the selection criteria, with retrieval accuracy running behind both. The accuracy problem only shows up after the system is already live. There is a known fix for this, a governed context layer every agent reads from instead of guessing. Vendors are racing to roll out context platforms while most enterprises are still figuring out what it is. 75% don't have an agentic context layer yet The context layer is meant to be a shared model of what business data actually means, built once and referenced consistently instead of re-derived by every agent that touches it. The VentureBeat research shows the enterprise response to that idea is broad but unfinished. Twenty-five percent of respondents run one in production. Thirty-four percent are building one right now. The remaining 41% have not started. Among companies already building or running a governed context layer, 78% report a confident-wrong failure — an AI agent that answered with total certainty and was still wrong. Among companies with no plans to build a layer, only 20% report the same thing. Companies that already got burned are far more likely to be building the fix. Companies that haven't been burned yet see no urgency. What governed context looks like when someone actually builds one Every major data and AI platform vendor is now building some version of this layer, and they are not converging on the same architecture. DataHub is treating catalog metadata and years of analyst query behavior as a knowledge source, then keeping it current as a living system rather than a static wiki. Microsoft's Fabric IQ is building a business ontology that any agent, not just Microsoft's own, can query over MCP. Couchbase is pushing agent memory and context retrieval down to the edge, arguing the operational database is a more natural home for it than a search or analytics layer bolted on after the fact. Pinecone's Nexus is compiling structural logic into the metadata layer ahead of runtime, betting that agents need pre-built structure more than they need faster search. Snowflake runs a two-layer system, Horizon Context for customer-managed definitions and Cortex Sense for context the platform infers on its own. Oracle's Unified Memory Core takes the opposite approach, folding vector, graph and relational data into one transactional engine so there is no sync layer left to go stale. Google's Knowledge Catalog mines query logs and usage patterns to curate semantic context automatically. AWS's Context service makes the same bet, a knowledge graph that gets smarter from how agents actually use it rather than from manual re-curation. Analysts converge on one diagnosis The vendor approaches differ. What analysts and practitioners have told VentureBeat about the underlying problem, across a run of interviews this year, does not. When DataHub's context layer push landed this spring, Constellation Research VP and principal analyst Michael Ni framed the stakes in blunt terms. "Whoever controls runtime context controls the AI decision layer for enterprise data," Ni said. He was equally direct about how far any single product actually gets a buyer. "Vector memory isn't business meaning, business meaning isn't governance and governance isn't execution," Ni said. In the same interview, BARC analyst Kevin Petrie pointed to a narrower but concrete gap. Most context platforms concentrate on structured tables, he said, which give agents trusted facts but miss the harder, messier context locked in documents and unstructured content, exactly the material a business actually runs on day to day. Stephanie Walter, practice leader for AI Stack at HyperFRAME Research, made a related point earlier this year when VentureBeat asked her about enterprise context fragmentation . "The market is converging on the same conclusion," Walter said. "Agents don't just need more tokens or better models. They need governed, current, low-latency context." She made a similar case in an earlier review of Pinecone's Nexus launch , careful not to overstate how new any of this is. Nexus, she said, "shifts knowledge work from runtime chaos to pre-compiled structure. But it's an evolution of RAG architecture, not a complete reinvention." Gartner's Arun Chandrasekaran, reviewing the same launch, offered the more forward-looking read. Agentic AI, he said, is moving from pure information retrieval toward a reasoning architecture, one where long context works as short-term memory and a vector database functions as deep storage underneath it. The fragmentation problem shows up hardest at the practitioner level, where separate tools for retrieval, memory and access control were never built to agree with each other. Steven Dickens, CEO and principal analyst at HyperFRAME Research, put it bluntly after Oracle's AI database push landed this spring. "Data teams are exhausted by fragmentation fatigue," Dickens said. "Managing a separate vector store, graph database and relational system just to power one agent is a DevOps nightmare." Matt Kimball at Moor Insights and Strategy, in that same story, put the production reality more simply. Getting an agent working is not the hard part, he said. The struggle is running it in production, where the goal becomes removing the distance between data and execution rather than adding another layer on top of it. What this means for enterprises Here's what this adds up to for enterprises building on this layer. Retrieval alone will not close the context gap. RAG is the default source for context in most enterprises today, and it is also the layer most closely associated with the confident-wrong-answer failure. Adding more documents or a bigger index does not fix a definition that is inconsistent across systems. The semantic context layer is where the budget is actually moving, even where it hasn't shipped. Fifty-eight percent of enterprises are already engaged — building or in production — but only 25% have actually gotten a layer live. That gap shows where enterprises have decided to spend, not where they've arrived. No single vendor owns the architecture yet, and that is likely to stay true for a while. Enterprises evaluating this layer should expect to integrate rather than pick a single winner, at least for the next several quarters. The buying decision is happening this year, and it is concentrated among the companies already burned by it. Fifty-seven percent of enterprises plan to switch or add a retrieval or context platform within the next twelve months. That intent is not spread evenly. Enterprises that reported a repeat confident-wrong failure plan to switch or add a provider at roughly 81%, against 32% among enterprises that never hit the problem. The companies shopping for new context tooling right now are largely the ones whose agents already got it wrong. The agents are already running. The context underneath most of them is still being built, and the vendor selling the fix is being chosen this year. This data will be part of a broader conversation at VB Transform 2026 on July 14 and 15 in Menlo Park: the context gap enterprises are racing to close, and which of the emerging approaches — governed semantic layers, hybrid retrieval, provider-native bundles — actually holds up in production.
- Dexmal Launches Embodied MaaS and DexOS, Solving Model Scaling Into Real-World Scenarios
Dexmal unveils DM0.5 foundation model, DexOS operating system, and embodied MaaS platform at Action developer conference, aiming to create the Android of robotics.
- AI gets a cerebellum
AI gets a cerebellum EurekAlert!
- Abu Dhabi launches control room for real-time monitoring of autonomous vehicles
Integrated Transport Centre launches central control room to oversee, monitor autonomous vehicle operations across Abu Dhabi
Score: 63🌐 MovesJul 10, 2026https://www.khaleejtimes.com/uae/transport/abu-dhabi-control-room-monitoring-autonomous-vehicles - Wall Street is debating the AI buildout. Enterprises just answered: 86% say their GPUs run at half capacity or less
Enterprise companies are running AI agents ahead of the controls needed to manage them — and they deployed that way knowingly. That is the central finding from VentureBeat Research's June survey of 573 technical leaders at companies with 100 or more employees, fielded across five parallel surveys of the agentic stack. Enterprises are now retrofitting to catch up with their own standards, and they are budgeting for it: Roughly six in 10 enterprises plan to switch or add vendors in each of five control layers within the next 12 months, and roughly a third — depending on the layer — plan to move within the quarter, the research finds. There are five main layers where enterprises are building: identity for agents (which agent is allowed to do what, under whose credentials); evaluation of agent output (whether the work is any good); cost telemetry (what each agent costs to run); the context layer (the business data and definitions agents draw on to answer); and the orchestration control plane (the software that coordinates multi-step agent work). Enterprises are already paying the price for deploying agents ahead of adequate control functions. Fifty-four percent of companies had an agent security incident or near-miss caught before harm in the past 12 months. Twenty-seven percent exercise only reactive control of agent spend — they learn what an agent costs when the invoice arrives, with no per-agent budget or ceiling in place. Here are the five findings that anchor the set — one finding per layer of the tech stack — and what the data suggests doing first in each. Expensive hardware is idle: 86% of GPU operators report utilization of 50% or less Eighty-six percent of enterprises that run their own GPUs report utilization of 50% or less. Wall Street has spent the quarter debating whether the AI buildout is overbuilt. This is buy-side measurement, from the enterprises doing the buying, and the research says the most expensive hardware in buildings of these enterprises runs at no more than half its capacity. The measurement gap compounds it: A minority 44% rigorously track what their AI compute actually costs and returns. Everyone else is only estimating. And the enterprise shopping process continues regardless: 45% of these enterprises say the emerging compute option they are most likely to evaluate in the next 12 months is an AI-specialized cloud (CoreWeave, Lambda, Crusoe, Nebius). However, under 2% of these enterprises report using one of these neoclouds today. Moreover, roughly one in three companies appears to be considering a hedge against Nvidia: Asked which emerging compute option they are most likely to evaluate in the next 12 months, 32% of enterprises named non-Nvidia accelerators (AWS Trainium, Google TPUs, AMD), while 28% named next-generation Nvidia GPUs. The data suggests that enterprises should measure the utilization and per-workload cost of the GPUs they already own before committing budget to new compute — whether that's an AI-specialized cloud contract, new accelerators, or more GPUs. Most deployed "agents" do single-prompt work: 71% say a quarter or fewer complete multi-step tasks on their own Seventy-one percent of enterprises say a quarter or fewer of their deployed "agents" can complete multi-step work on their own; the rest are single-prompt chatbots. Only 10% say true agents are the majority of what they run. To be sure, the respondents reported that they are in a position to know these things: 81% said they recommend or decide AI purchases at their companies. That finding — that most agents are actually just chatbots in trenchcoats — lands amid adoption claims across the industry running well ahead of what enterprises are actually running. Gartner predicted 40% of enterprise applications will be integrated with task-specific AI agents by the end of 2026, up from less than 5% in 2025. It also warned that the most common misconception is referring to these AI assistants as agents, a misunderstanding known as "agentwashing." Meanwhile, Zapier's enterprise survey said 72% reported deploying or testing autonomous agents; and Writer's 2026 survey has 97% of executives saying their company deployed AI agents in the past year. Those surveys asked whether companies have deployed something called an AI agent, and companies said yes. Our survey asked the people running those deployments a harder question: Of the agents you have in production, how many can complete a multi-step task without a person driving each step? The gap matters for two practical reasons. First, the inflated adoption figures are the benchmark boards and vendors use to pressure technical leaders into moving faster — and this data says the real bar is far lower than the headlines suggest. Second, the label determines the bill: A single-prompt chatbot with a human reading every answer needs none of the identity, evaluation, and cost controls this report covers, while a true multi-step agent needs all of them. 66% let agents push to production on automated evals alone — or are engineering toward it. 5% fully trust those evals Two-thirds of enterprises fall into one of two camps: 34% already allow an AI agent to push a code or system change to production based on automated evaluation results alone, with no human reviewing it, and another 33% are actively engineering their pipelines to allow that within the next 12 months. Only five percent fully trust the automated evaluations that would make that decision. The distrust is earned. Half of enterprises shipped an agent that passed internal evaluations and then caused a customer-facing failure in the past year; a quarter watched it happen more than once. Asked to name the biggest weakness in their current evaluations, more enterprises chose “poor alignment with real-world outcomes” than any other answer — 29% of respondents. And most of the checking happens before an agent ships, then stops. Once agents are live with real users, only 23% of enterprises run real-time quality checks on the answers those agents produce. Another 51% monitor system health only — uptime, request traces, and gateway logs — which tells them the agent is running, and nothing about whether its answers are right. The first move: Before removing human review from any workflow, test your evaluations against production outcomes rather than internal benchmarks, and instrument answer quality, not just uptime. This finding is explored in more depth in VentureBeat's related coverage of the evaluation gap , which found that larger enterprises are moving faster toward zero-human deployment while also failing more often — and outlines a regression-testing framework built on production outcomes rather than internal benchmarks. 69% run credential sharing somewhere in the agent fleet — and those companies get hit far more often Sixty-nine percent of companies allow agent credential sharing somewhere in their agent fleet during runtime – meaning multiple agents operating under one API key or service account. Those companies were far more likely to get hit: Organizations with credential sharing anywhere in the fleet experienced a security incident or near-miss at a 63.5% rate (47 of 74), against 40.9% (9 of 22) where every agent has its own scoped identity. The takeaway for enterprises is this: Give every agent its own scoped identity, starting with the agents that touch production systems. 57% traced a confident, wrong agent answer to their own missing or inconsistent business context Fifty-seven percent of enterprises traced at least one confident, wrong agent answer in the past six months to missing or inconsistent business context: wrong metrics, stale definitions, absent documents. Most of them watched it happen more than once. Most enterprise companies are fixing this, even though they’ve moved forward with agent deployment already: 25% already run a governed semantic layer, or one governed definition of the business that every AI reads from, in production. However, 34% are still building one, and 41% haven't started. The takeaway: Govern the definitions your agents answer from, metrics and entities first, before scaling the agents that depend on them. The quarter where agent technology “portability” became a priority One more shift is worth reporting with its limits stated plainly. In our spring orchestration survey wave, the top concern about provider-controlled orchestration was security and permissioning limits (32%). By June, vendor lock-in led at roughly a third, with security limits at 28%. Those are two snapshots one quarter apart, and here’s one possible explanation for why portability became a top issue for enterprises. Our June survey went into market after a June 12 U.S. Commerce Department export order took Anthropic's Claude Fable 5 offline for enterprises for roughly three weeks. Meanwhile, Chinese company Z.ai released GLM-5.2's open weights under an MIT license on June 16 at roughly one-sixth of GPT-5.5's price; and Tencent's Hy3 arrived July 6 under Apache 2.0; and OpenAI previewed GPT-5.6 on June 26 to a small group of government-vetted partners, opening it broadly on July 9 after the government's review cleared. The open-weight releases in particular promise enterprises more control over their agents, and while we haven't established a causal link here, the timing is worth noting. The posture data matches the mood: 51% now expect their primary control plane for enterprise agents to be hybrid — provider-native plus external orchestration — by the end of 2026, up from 34% in the spring survey wave. Enterprises reporting that they rely purely on provider-managed agent services fell from 12% to 7%. Five layers, no incumbents, 12 months The synthesis across all five surveys reveals a huge “buying” window. In each of the five control layers, 57% to 64% of enterprises plan to switch or add vendors within 12 months — 64% in infrastructure and in evaluations, 59% in agent security, 57% in retrieval and context — and 26% to 38%, depending on the layer, plan to move within a quarter. No layer has an established incumbent: The most common evaluation tooling is the model provider's built-in evals, tied with no dedicated tooling at all (17% each); 82% of respondents name provider-native or hyperscaler controls as their primary agent security layer; and provider-native retrieval leads the context technology layer (RAG, etc) as well. Most enterprises are defaulting today to the built-in tools that ship with the big AI platforms they already use: Anthropic, OpenAI, Google, Microsoft, and AWS. That holds true across every one of these agentic technology layers: enterprises are looking to their primary cloud and model providers to supply the guardrails, evaluations, and retrieval solutions already bundled into those providers' offerings. Those defaults are winning on convenience, and they're also what the coming spending decisions will test. The survey didn't ask which direction that money moves — toward the platforms' built-in tools or toward the specialists challenging them — which is exactly why every contract in these five layers is worth watching over the next four quarters. The Q3 survey wave will measure whether the enterprises made good on these budget plans: whether their agents gained scoped identities, whether evaluations got tested against production outcomes, whether GPU utilization rose, and whether the semantic layers under construction shipped. VentureBeat will release the full Q2 reports across all five VB Pulse trackers at VB Transform , July 14–15 at Hotel Nia in Menlo Park, where we convene enterprise technical leaders building autonomous agents in production. Disclosure: VentureBeat produces both this research and VB Transform
- Reeves to launch City ‘skills compact’ committing firms to retrain staff in AI
Exclusive: Plan to improve skills of thousands of financial sector workers to keep pace with tech revolution Chancellor Rachel Reeves is to announce a new City “skills compact” that will commit firms such as Barclays and Lloyds to retraining thousands of financial sector workers for the AI revolution. The financial services skills compact will be launched on Tuesday, during what is likely to be Reeves’s final Mansion House speech to City bosses before Andy Burnham’s expected takeover of No 10. The government-backed initiative will commit employers to improving workers’ skills and helping them “keep pace” with significant technological changes that have prompted fears of mass redundancies. Continue reading...
Score: 63🌐 MovesJul 10, 2026https://www.theguardian.com/business/2026/jul/10/rachel-reeves-city-skills-compact-ai-training - Tech Launches (July 10, 2026): ASUS Vivobook AI PCs, Dell Pro Precision workstations, and more
Tech Launches (July 10, 2026): ASUS Vivobook AI PCs, Dell Pro Precision workstations, and more
- Africa’s data centre evolution: AI, edge computing and new energy demands
As artificial intelligence races from experimentation to core infrastructure, it brings with it new demands on data centres worldwide.
- The future of CRM: AI-native, consolidated, and frictionless
Talk to anyone in sales, marketing, or ops long enough, and the CRM conversation runs in a loop. First, it’s “why are we paying this much for a system that basically does nothing?” Then the company adopts a new platform, and for a quarter or two there’s relief: the pipeline is clean, reports actually load, […] The post The future of CRM: AI-native, consolidated, and frictionless appeared first on e27 .
- Teaching AI the laws of navigation: New physics guided detector catches GNSS spoofing even in never before seen attacks
Teaching AI the laws of navigation: New physics guided detector catches GNSS spoofing even in never before seen attacks azcentral.com and The Arizona Republic
- Adam Mosseri says Instagram has 'reined in' AI costs after shutting down 'the silly things' that were burning tokens
Adam Mosseri says Instagram has 'reined in' AI costs after shutting down 'the silly things' that were burning tokens Business Insider
- Haves, have-nots and know-nots: Inside AI's new class divide
A staggering class divide now separates how Americans experience artificial intelligence : For frontier power users, AI feels like a r evolution : a force capable of conjuring companies, building software and solving complex problems at warp speed. For the average person, it feels more like an evolution : a smarter search bar, a faster inbox, an ambient tech layer that saves time — but not much else. Why it matters: Trillions of dollars in economic value — and the livelihoods of millions of workers — are being staked on a technology that most Americans neither trust nor fully understand. It's a new chapter in America's digital divide — the AI "haves," "have-nots" and "know-nots" — with profound implications for the future of wealth, work and power. Zoom in: The newest frontier models are designed for an agentic world of coding, research and cybersecurity that most Americans will never see, let alone operate. OpenAI's Sol and Anthropic's Fable now sit atop the pyramid of elite AI obsession, prized for running long coding and research loops with minimal human intervention. Prominent developers have spent the week personifying the two models — debating their temperaments, work ethics, even their personalities, the way sports fans argue over rival athletes. "My overall feel is that Fable is a 'wise owl' who is very thoughtful and very well spoken," tweeted AI researcher Peter Gostev. "GPT-5.6-Sol is like a rottweiler who will grab the problem by the throat and not let go until it is done." Reality check: The people fluent enough to judge Sol against Fable on a coding benchmark are a tiny slice of the country. For most Americans, those names and metrics mean nothing. Millions of people encounter AI passively or unknowingly — through search summaries, AI-generated content, customer-service bots and invisible features inside apps. Nearly half of U.S. adults now use AI chatbots, but the most common use is basic information search — the same job Google has done for two decades, a world away from autonomous coding agents. OpenAI counts more than 50 million paying subscribers in its weekly ChatGPT user base of more than 900 million. The population running agentic coding tools is a fraction of that fraction. Between the lines: Even among the elites living the frontier AI revolution, there's a pecking order. Sol began as a restricted preview for OpenAI's trusted partners and select organizations before broader rollout, making early access itself a status marker inside AI circles. Fable was pulled offline globally for nearly three weeks in June under U.S. export controls, while its more powerful sibling, Mythos , remains restricted to a small number of trusted organizations. The result is a hierarchy inside the hierarchy: free users, paid users, power users, preview users and an insider class testing capabilities the rest of the world can only read about. The big picture: The AI industry ultimately needs broad social permission for the transformation it's selling: more data centers, deeper workplace automation, and AI embedded in schools, government and daily life. Yet as AI adoption has climbed, trust has fallen: 63% of Americans say AI is advancing too quickly, and just 16% expect it to benefit society over the next 20 years, according to Pew Research . The clearest gains are being captured by investors, tech giants and power users, while ordinary Americans are being asked to absorb the disruption to jobs, energy and information feeds. What to watch: The Trump administration's Labor Department published a national AI literacy framework in February, aimed at helping workers "share in the prosperity that AI will create." OpenAI, Anthropic, Microsoft and Amazon helped pool $500 million in June for RAISE US , a workforce retraining initiative led by former Commerce Secretary Gina Raimondo and former Indiana Gov. Eric Holcomb. But basic literacy efforts can only go so far: Frontier users have better tools, earlier access, deeper technical context and hundreds of hours of trial-and-error with systems that change every few weeks. Flashback: A century ago, electricity exposed a similar divide between Americans living in the modern age and those watching it from the dark. By 1930, nearly 90% of urban homes had electricity, compared with roughly 10% of farms. Private utilities had little incentive to wire rural customers spread across miles of unprofitable territory. It took the New Deal's Rural Electrification Administration — and years of federal loans — to bridge a gap the market had left behind. AI's divide may be even harder to close: Frontier access is scarce and expensive, and even where it's free, most people don't know what to do with it. The bottom line: The AI industry is betting on inevitability. But history suggests technological revolutions need legitimacy, too.
- Google deepens Gemini’s presence in the ads business
The 'Business Agent for Leads' is a Gemini-powered chatbot trained on an advertiser's website directly embedded inside a Search ad, allowing users to seek answers about the brand & its products and convert those into leads. The post Google deepens Gemini’s presence in the ads business appeared first on MEDIANAMA .
- Wall Street climbs, oil slides as investors bet on AI growth over Middle East tensions
Wall Street climbs, oil slides as investors bet on AI growth over Middle East tensions Reuters
- Beyond AI models: Why data infrastructure is now a priority for enterprises
As enterprises move beyond AI pilots, the focus is shifting from adopting larger AI models to building a real-time, reliable data infrastructure that can support AI at scale
- Mistral joins rush to develop AI for robots
Mistral joins rush to develop AI for robots InfoWorld
Score: 61🌐 MovesJul 10, 2026https://www.infoworld.com/article/4195667/mistral-joins-rush-to-develop-ai-for-robots.html - Dell Technologies brings AI-ready workstations and Deskside Agentic AI to India with the Pro Precision portfolio
Dell Technologies has announced its new Dell Pro Precision portfolio in India, a lineup of AI-ready workstations designed for professionals who demand high performance. The post Dell Technologies brings AI-ready workstations and Deskside Agentic AI to India with the Pro Precision portfolio appeared first on Express Computer .
- LG Electronics begins early production of humanoid robot actuators
LG Electronics begins early production of humanoid robot actuators 매일경제
- Weekly Must-Read: The AI Token Takeover in China
Weekly Must-Read: The AI Token Takeover in China Caixin Global
Score: 60🌐 MovesJul 10, 2026https://www.caixinglobal.com/2026-07-10/weekly-must-read-the-ai-token-takeover-in-china-102462487.html - IBM grows mainframe family with rack, frame models targeting AI, hybrid clouds
IBM is looking to expand the reach of its foundational mainframe portfolio by adding new single frame and rack mounted versions of its Z and LinuxONE systems. The IBM z17 portfolio adds a single frame and rack mount versions that bring mainframe capabilities into smaller, customizable footprints. The LinuxONE Rockhopper family gets a single frame and rack mount models, plus a new Express rack mount offering, that target new and smaller clients, according to Tina Tarquinio, chief product officer, IBM Z & LinuxONE. Specifically, the new hardware includes: z17 single frame is a fully packaged box in an IBM rack with intelligent power distribution units, delivered as a complete enclosed unit ready to deploy at the edge or other strategically important customer sites. z17 rack mount lets customers install IBM Z components directly into their own industry-standard rack, with built-in flexibility for co-location with other technologies. LinuxONE Rockhopper 5 is a multi-drawer LinuxONE system for high-density workloads, with on-chip AI acceleration, confidential computing, and postquantum cryptography available in both single frame and rack mount configurations. Rockhopper 5 rack mount and Express offerings deliver enterprise-grade Linux, confidential computing, and on-chip AI acceleration in a compact 18U configuration. Designed for organizations supporting a smaller set of workloads, the offering provides a cost-efficient entry point that can scale as business grows, while prioritizing security, resiliency, and performance. IBM The new IBM z17 and IBM LinuxONE 5 Rockhopper configurations support up to 82 cores and 18 TB of memory across two processor drawers, representing about a 20% increase in core count and 12% increase in memory capacity over current systems, IBM stated. Single processor capacity of an IBM z17 ME2 provides full speed IBM z/OS configurations including 10% greater throughput per core than IBM z16 A02 with some variation based on workload and configuration, according to Tarquinio. Both systems feature a 5.5 GHz IBM Telum II processor and a built-in AI accelerator that IBM says will let customers run more than 450 billion inferencing operations in a day with one millisecond response time. In addition, the 32-core Spyre AI accelerator is designed to handle all manner of AI workloads. The idea is to bring the core strengths of IBM Z to a broader range of deployment models while offering the security, resilience, and performance enterprises depend on, Tarquinio said. “As always, we’re continuing to innovate to deliver more with less, including up to 20% more capacity than IBM z16 to help process transactions faster and support growing AI-driven workloads,” Tarquinio said. “Even the newest and smallest member of the IBM z17 family delivers the performance, efficiency, and scalability organizations need as they balance growth ambitions with real-world resource constraints.” The Linux-based system, Rockhopper 5 is for organizations that have moved past the evaluation question and are ready to consolidate a substantial portion of their x86 estate, said Marcel Mitran, IBM Fellow and CTO of IBM LinuxONE. Rockhopper 5 is designed to bring a smaller physical footprint and a software licensing model that reflects actual workload boundaries rather than physical server counts, Mitran said. The LinuxONE 5 Express is a preconfigured system designed to get organizations running on LinuxONE quickly, with a defined bill of materials and a predictable starting cost, on the same architecture that the largest enterprises in the world depend on, Mitran said. “It is built for organizations that want to consolidate a modest x86 estate, evaluate LinuxONE for the first time, or deploy a specific workload such as digital assets, AI-infused transaction processing, or confidential computing, without committing to the footprint of the larger model,” Mitran said. Some of the mainframes’ software features were also bulked up. For example, IBM said that Post Quantum Cryptography security is now standard on the z17 and LinuxONE Rockhopper 5 systems letting customers start to utilize cryptography to protect core resources for the future. The idea is to help customers protect long-lived, mission-critical data while reducing the cost and complexity of future cryptographic migration, IBM stated. In that vein, IBM said it was bringing Crypto Discovery & Inventory, which lets security teams see what has been encrypted across the enterprise. In addition, IBM announced an Infrastructure Management for Z and LinuxONE package that would let customers administer, monitor, automate, and provision IBM Z and LinuxONE systems from a central location. IBM said it wants to reduce operational complexity for customers by making automating day-to-day operations to ultimately lower administrative costs and concerns. With the new flexible form factors, IBM continues to target hybrid and AI infrastructure buildouts with the Big Iron. In the AI world, the z17 is being utilized for AI inferencing, transactions, training, and key security applications such as fraud detection and insurance claims. “Enterprise infrastructure is entering a new phase. Organizations need platforms that can support AI-driven growth while navigating resource constraints, evolving business requirements, and increasingly complex hybrid environments,” Tarquinio said. “They are being asked to deploy new AI capabilities while learning new skills, controlling operational costs, and maximizing the value of existing applications and infrastructure.” A recent IBM Institute study on mainframe usage stated that embedding mainframe to support AI in executing transactions is not temporary: 75% of executives expect mainframe-based applications to remain central to digital transformation, and 60% say mainframe-based platforms are essential to enabling AI innovation. ”Mainframe-anchored systems of record are becoming systems of intelligent execution—not as general‑purpose AI platforms, but as environments where AI acts directly within transactions and in support of them,” the study reported. Gartner wrote in its “ The State of the IBM Mainframe in 2026 ” report that IBM’s willingness to make significant investments ensure the mainframe modernizes to remain a vital and thriving component of enterprise IT. “Most mainframe customers are now prioritizing the reduction of technical debt and adopting platform innovations to future-proof their mainframe environments for the coming decade,” Gartner wrote. The new z17 single frame and rack mount configurations, LinuxONE Rockhopper 5, and LinuxONE 5 Express will all be available August 12, 2026. IBM Infrastructure Management for IBM Z and IBM LinuxONE will be available August 14.
- The AI stack trap: Why more AI tools aren’t translating into more growth
Every week, a new AI tool launches, promising to transform marketing. One that writes content, another that generates videos, a third that automates outreach. Finally, one that builds reports. For a while, it felt like the companies that adopted the most AI would win. But something interesting has happened over the last two years. Many businesses […] The post The AI stack trap: Why more AI tools aren’t translating into more growth appeared first on e27 .
Score: 60🌐 MovesJul 10, 2026https://e27.co/the-ai-stack-trap-why-more-ai-tools-arent-translating-into-more-growth-20260706/ - Before Apple Lawsuit, a Growing Unease at iPhone Maker Over OpenAI
Before Apple Lawsuit, a Growing Unease at iPhone Maker Over OpenAI The Information
Score: 60🌐 MovesJul 10, 2026https://www.theinformation.com/articles/apple-sues-openai-trade-secret-theft - OpenAI Is Trying to Rip Our Screens Away With Its New Device
OpenAI Is Trying to Rip Our Screens Away With Its New Device Business Insider
Score: 60🌐 MovesJul 10, 2026https://www.businessinsider.com/open-ai-new-device-screen-addiction-jony-ive-2026-7 - AI boom puts Big Tech's transparency to the test
After years of touting ambitious climate goals, big tech companies are now facing new scrutiny over what they disclose about AI's environmental footprint. Why it matters: The AI boom is turning a handful of tech companies into defining players in the debate over electricity and water use. Their willingness to disclose these impacts is becoming almost as important as the impacts themselves. Driving the news : New environmental reports from Google , Amazon and Microsoft show emissions and water use continuing to rise as AI infrastructure expands — while revealing differences in what the companies disclose. "There is a bit of a reluctance to share a lot of things in this competitive dynamic and also with these being public companies," said Boris Gamazaychikov, who co-founded Sustainable AI Group, a new research and advisory firm that helps companies address the environmental impacts of AI. The big picture : Transparency is emerging as a key response to growing opposition to AI focused on data centers' energy and water toll. Amazon, Google, Microsoft and Meta account for roughly two thirds of the data-center power capacity in a top 15 ranking by financial firm Jefferies, so what they do influences the entire market. Friction point: United Nations Secretary-General António Guterres this week called on tech companies to publicly disclose the "full footprint" of their data centers, including carbon, water and land use. "AI may feel intangible — but its footprint is not," Guterres said , reiterating the U.N.'s AI Environmental Transparency Initiative unveiled last month. State of play: Top executives at these tech companies generally say they support transparency, but their reporting varies widely once you dig into the disclosures. No law requires companies to detail many of these AI-related environmental metrics, and no common reporting standard exists for those that are disclosed. What they're saying: Kara Hurst, the chief sustainability officer at Amazon, said in an interview before the UN announcement that all companies building data centers should disclose their environmental footprints. "We'd love to see one holistic interoperable standard," Hurst said. Catch up fast: Comparing the companies' disclosures is a complicated apples-to-oranges exercise. Alex de Vries-Gao, a researcher at VU Amsterdam university and founder of online platform Digiconomist, reviewed the latest disclosures and ranks them, on transparency alone, as Meta first, followed by Google and Microsoft in a "toss-up" for second, and Amazon last. Meta's 2026 report is expected later this year, likely in the late summer or early fall. Zoom in : Transparency doesn't necessarily equal top performance, and no company leads across every measure, according to de Vries-Gao's analysis published earlier this year and these latest reports. Google and Meta disclose the strongest energy-efficiency metrics, according to the analysis. Microsoft newly disclosed specific water and power metrics at individual data center locations in its latest report, which de Vries-Gao said represents "a significant improvement in transparency." Amazon reports the strongest water-efficiency figure, though it ranks last overall because it provides the fewest metrics, including how much electricity it consumes, de Vries-Gao said. Google doesn't publish a companywide water-efficiency metric, arguing that a global average masks its local risk-based approach. Using Google's own data, de Vries-Gao estimates it would rank last. Only Meta discloses the water associated with electricity generation — a potentially significant omission by the others because that use typically far exceeds water consumed at the data center itself. Reality check: Disclosure is only one measure. Companies are also investing billions in clean-energy projects whose climate benefits could take years to materialize. On that front, Google is largely considered the leader, alongside Microsoft. How it works: AI infrastructure needs both electricity and cooling. As data centers expand, companies are increasingly trading water-intensive cooling for more energy-intensive alternatives. Between the lines: Generating electricity from fossil fuels and nuclear power requires large amounts of water, and some experts say tech companies should account for that. Judging by Meta's 2025 disclosures, this indirect water use was roughly 24 times larger than the amount used at its data centers, de Vries-Gao calculated . "We don't know whether that ratio is representative of the rest of the industry because the other companies don't disclose comparable figures," he said. Not all environmental advocates agree this level of disclosure is warranted . "It's probably not totally fair" to hold them accountable for water from associated electricity, said Peter Gleick, co-founder of the Pacific Institute, a California-based water research nonprofit. "But it's a legitimate question and would accelerate a push to switch to low-water using renewable energy sources." What we're watching : Gamazaychikov, of Sustainable AI Group, says pressure from these tech companies' customers, like consumer brands, will likely prompt more transparency and accountability in the absence of government action. The bottom line: "I don't think anything regulatory is going to happen soon," he said. "It's going to take business to make this happen."
Score: 60🌐 MovesJul 10, 2026https://www.axios.com/2026/07/10/ai-big-tech-transparency-electricity-water-use - The Billion Dollar Misfire: Why the AI boom keeps breaking in the middle
The Billion Dollar Misfire: Why the AI boom keeps breaking in the middle IT Pro
Score: 60🌐 MovesJul 10, 2026https://www.itpro.com/technology/the-billion-dollar-misfire-why-the-ai-boom-keeps-breaking-in-the-middle - IBM researchers break up with traditional transformers in new gen AI model architecture
IBM researchers propose a novel architecture for light-weight generative AI models.
Score: 60🌐 MovesJul 10, 2026https://www.thestack.technology/ibm-paper-new-generative-ai-light-weight-model/ - Global equity fund inflows surge to three-week high on AI optimism
Global equity fund inflows surge to three-week high on AI optimism Reuters
Score: 60🌐 MovesJul 10, 2026https://www.reuters.com/world/china/global-markets-flows-graphic-2026-07-10/ - AI skills now listed in 73% of tech job postings
Highly regulated industries working to shape their AI implementation plans outpace other industries in the search for talent, according to Dice.
- A.I. Has Rewarded Investors. It May Now Pose Their Greatest Risk.
Near the midpoint of the year, stocks and bonds both report good returns. But the global stock market has become highly concentrated, our columnist says.
Score: 60🌐 MovesJul 10, 2026https://www.nytimes.com/2026/07/10/business/ai-stocks-bonds-investing-markets.html - Tata Communications builds the foundation for Asia’s AI future
Tata Communications builds the foundation for Asia’s AI future verdict.co.uk
Score: 60🌐 MovesJul 10, 2026https://www.verdict.co.uk/tata-communications-foundation-asia-ai-future/