AI News Archive: August 13, 2026 — Part 5
Sourced from 500+ daily AI sources, scored by relevance.
- Tencent Shares Fall as AI Spending Squeezes Profit
Tencent Shares Fall as AI Spending Squeezes Profit Caixin Global
Score: 45🌐 MovesAug 13, 2026https://www.caixinglobal.com/2026-08-13/tencent-shares-fall-as-ai-spending-squeezes-profit-102473810.html - Alibaba Cloud Launches Qwen AI Arena for Real-World Agent Testing
Alibaba Cloud has launched Qwen AI Arena, a challenge and evaluation platform for AI agents. The platform creates tasks based on real business scenarios and provides developers with models, runtime environments and evaluation tools to submit and test agent solutions. Its first challenge focuses on cross-border e-commerce. Participants must generate product listings for the US, […]
Score: 45🌐 MovesAug 13, 2026https://technode.com/2026/08/13/alibaba-cloud-launches-qwen-ai-arena-for-real-world-agent-testing/ - Powering the AI Revolution: Efficient Strategies for High-Density Data Center Environments
Artificial intelligence (AI) is here. From machine learning to new capabilities like generative AI (GenAI) powered by deep learning neural networks,
- Robot reaches 4,500-year-old sealed barrier inside Egypt’s Great Pyramid
A robot explored a hidden passage within Egypt's Great Pyramid of Giza. It reached a sealed barrier which may be 4,500 years old. This discovery has renewed questions about the ancient monument's hidden contents. Researchers used modern technology to examine areas difficult for people to access. Further study could reveal new details about the pyramid's design and construction.
- Rambler, Magic Capture: All the new AI features coming to Google Pixel 11 series
Rambler, Magic Capture: All the new AI features coming to Google Pixel 11 series
- Nvidia CEO Jensen Huang says AI bloodbath fears confuse jobs with tasks
Nvidia CEO Jensen Huang says the AI jobs debate is broken because people confuse tasks with jobs. Jensen Huang argues AI automates tasks, not entire roles, calling the AI job loss narrative "exactly backward." He cited radiology and software engineering as fields disrupted yet still hiring. His optimism clashes with Dario Amodei's white-collar bloodbath warning, though labour data on paralegals and manufacturing complicates Huang's case.
- Delhi HC Justice Prathiba Singh questions mandatory AI disclosure for lawyers
Delhi HC Justice Prathiba M Singh questions mandatory AI-use disclosure by lawyers, warning it may add compliance without improving accountability. The post Delhi HC Justice Prathiba Singh questions mandatory AI disclosure for lawyers appeared first on MEDIANAMA .
Score: 45🌐 MovesAug 13, 2026https://www.medianama.com/2026/08/223-justice-prathiba-singh-ai-disclosure-lawyers/ - Wealth managers woo OpenAI and Anthropic staff ahead of IPO windfalls
The rise of equity-rich tech workers at Anthropic and OpenAI is shifting negotiating power towards clients
Score: 45🌐 MovesAug 13, 2026https://www.ft.com/content/aa0ca006-5af4-4f94-9789-3e56f9f53e1d?syn-25a6b1a6=1 - Musk and Zuckerberg claw back into AI race with new model momentum
Elon Musk and Mark Zuckerberg have muscled their way back into AI's elite ranks, defying early obituaries to close the gap on a new generation of Silicon Valley startups. Why it matters: Recent gains by tech giants SpaceX and Meta — long stuck in AI's second tier — are putting new pressure on a hierarchy dominated by OpenAI and Anthropic. State of play: Both companies released models this week featuring a combination of performance and price that would have been hard to imagine from either lab a year ago. SpaceX's Grok 4.6 scored essentially even with OpenAI's GPT-5.6 Sol Max on the closely watched Artificial Analysis Intelligence Index , and just behind Anthropic's Fable 5 Max. Promising "something special," Musk said Grok 4.7 should arrive in three to four weeks and predicted it will "exceed all current models" after additional training on a massive trove of SpaceX data. Meta, meanwhile, is trying to squeeze the frontier from below. Its new models deliver competitive performance at dramatically lower cost — including Muse Glimmer, an open-weight system small enough to run locally on a laptop. Zuckerberg cast the push as "superintelligence for everyone" in a sweeping manifesto this week, reviving Meta's open-AI ambitions as the U.S. races to regain ground from China. Catch up quick: As OpenAI and Anthropic pulled ahead, Musk and Zuckerberg answered with the full force of their empires — spending billions on talent, infrastructure and acquisitions. Zuckerberg overhauled Meta's AI effort after the disappointing launch of Llama 4, then invested $14.3 billion in Scale AI and brought aboard its CEO, Alexandr Wang , to lead the push. Musk went even further, folding xAI into SpaceX and agreeing to acquire Cursor for $60 billion — bringing massive computing power, proprietary SpaceX data and a fast-growing AI coding platform under one roof. Between the lines: The AI race is starting to reward more than raw intelligence, as price becomes a decisive advantage for many users. "You don't need to be the highest performing, most expensive model in the market to be able to be successful," Sonali Basak, chief investment strategist at iCapital, told Axios. Basak said that raises a more consequential question for Musk and Zuckerberg: whether their AI advancements can "fuel the core businesses" that financed their comeback. Reality check: The frontier labs still hold pole position — with even more capable models waiting in the wings, including systems deemed too sensitive for public release. "I wouldn't say they've caught up to OpenAI or Anthropic," said John Belton, a portfolio manager at Gabelli Funds. A Meta employee told Axios the company still feels behind the frontier labs in model performance and development. What we're watching: Turbulence at Google could create an opening for either company to seize its place as Wall Street's public-market AI champion. Google had emerged as the established tech giant best positioned to challenge OpenAI and Anthropic, pairing elite talent with enormous resources and early momentum. That position suddenly looks shakier after an exodus of top AI talent — including Google's chief scientist — and setbacks involving its flagship model Gemini. The bottom line: OpenAI and Anthropic built their lead through years of technical excellence. Musk and Zuckerberg are nipping at their heels with decades of scale and resources.
- Buried in OpenAI's latest research: No correlation between AI use and revenue per employee
Buried in OpenAI's latest research: No correlation between AI use and revenue per employee Fortune
- Does Google even want to win at AI?
Is the shakeup at Google DeepMind a strategy or a crisis?
Score: 45🌐 MovesAug 13, 2026https://www.theverge.com/podcast/979370/google-deepmind-ai-race-lose-jeff-dean-demis-hassabis - UAE-built AI satellite Altair-1 heads to US ahead of October launch
UAE-built AI satellite Altair-1 heads to US ahead of October launch Gulf News
- Which AI models can you automate on Zapier? (GPT-5.6 Sol, Gemini 3.7 Flash, Opus 5, and more)
New AI models launch practically every week, and keeping up with which ones to use for specific workflows is a job in itself. Consider this article your living reference. At Zapier, we run every model through AutomationBench. It's our benchmark for testing how well models carry out multi-step workflows, not just static prompts. Below, I'll walk through every major AI provider available on Zapier, the models you can plug into your Zap workflows today, and what each one is best for based on Zapier
- 95% of enterprises delay AI projects amid infrastructure challenges: Cloudera Report
As enterprises scale artificial intelligence across their operations, limitations in legacy data architectures are prompting organisations to rethink how their infrastructure is designed and managed, according to a new global […] The post 95% of enterprises delay AI projects amid infrastructure challenges: Cloudera Report appeared first on Express Computer .
- At least six police forces have racked up contracts with Palantir worth £7.8m
At least six different police forces have recently used artificial intelligence (AI) investigation tools provided by Palantir Technologies, Computer Weekly and Good Law Project have found. Agreements between Palantir and these police forces have cost £7.8m, and the firm was close to signing an additional £50m contract with the Metropolitan Police before London’s mayor put it on hold. At least six confirmed police forces have had access to Palantir , while officers from several others appear to have limited access to applications in Palantir’s Nectar system. Palantir Nectar allows information sharing between local forces, regional taskforces and partner agencies using Palantir’s Foundry data integration platform. Nectar uses AI to process large volumes of personal data from crime records, seized digital devices and other unknown sources. Some police forces have been reluctant to disclose their involvement with Palantir. The company’s UK chief, Louis Mosley, said in mid-2025 that the firm was working with “almost a dozen” police forces. Over a year on, only a handful have been identified. Investigative outlet Democracy for Sale reported that a secret unit in the National Police Chiefs’ Council (NPCC) has been “systematically instructing” forces to withhold information “that may or may not be held in relation to Palantir software used for covert purposes”. The most recently identified force to test Palantir’s technology is Lancashire Police, which acquired Palantir’s Foundry software through Softcat, a UK-based IT infrastructure and software supplier. Foundry is the same software framework that underpins Palantir Nectar, the NHS Federated Data Platform and the Met Police’s internal staff compliance monitoring tool. Lancashire’s agreement was for a “proof of concept” the force was developing, at a cost of £860k. The force appears to have had access to Palantir’s software between March 2025 and June 2026, which included a contract extension. The force also said in freedom of information (FOI) disclosures that “there have been conversations with this supplier [Palantir] and others to look at opportunities on how these AI-driven technologies can assist digital policing”. However, Lancashire Police has kept no minutes of any meetings that may have discussed the project. The force was approached to ask whether its project was related to its regional taskforce and Nectar, but had not responded at the time of publication. Leicestershire Police also signed an £800,000 contract with Palantir in October 2024, as reported by The Leicester Gazette , which obtained the information under the Freedom of Information Act, following intervention of the regulator on behalf of the publication. The force is one of the few confirmed users of Palantir’s software in the East Midlands Special Operations Unit (Emsou), a regional taskforce tackling organised crime and terrorism. The Emsou is composed of Leicestershire, Derbyshire, Lincolnshire, Northamptonshire and Nottinghamshire police forces. According to meeting records obtained by Computer Weekly, all these forces have taken part in several cross-constabulary briefings on Palantir Nectar. Other forces in the Emsou neither confirm nor deny (NCND) responses – the tactic applied by the police chiefs’ disclosure blocking unit – to Computer Weekly’s freedom of information requests, or have ignored them entirely, when asked if they are using Palantir’s software. After approaching the respective press offices, Lincolnshire Police said Nectar does not “appear” to be in use by the force. Northamptonshire Police said officers have no “direct access” to the system and there are no plans to adopt it. Derbyshire and Nottinghamshire were both contacted to answer queries about possible access to Palantir Nectar, but did not respond by the time of publication. A spokesman for the Emsou did not answer questions about which forces in the regional unit get access to the system, but said: “We followed appropriate processes to secure services to support the delivery of operational policing.” Bedfordshire Police is leading a trial of Palantir Nectar in the Eastern Regional Special Operations Unit (Ersou). The trial was first reported by Liberty Investigates as a “real-time surveillance network” that monitors special category personal data such as race, religion, political beliefs and sexual orientation, and The i Paper reported that rape victims could have their data trawled by its AI. The Metropolitan Police described Bedfordshire’s Nectar programme as a “real-time data-sharing network across Bedfordshire, Cambridgeshire and Hertfordshire police, Regional Policing, UK Policing and partner agencies” when evaluating the launch of its own internal monitoring project. Documents obtained by Computer Weekly reveal the range of permissions granted to members of the Ersou to access Nectar’s internal applications. The unit appears to be granted access to live environment applications handling call data records, device analytics, thematic scanning, operations management and more. A spokesperson for the force has confirmed that seconded officers from other police forces in the Ersou “may be provided access to Nectar applications using role-based access control in accordance with information security and access management policies and procedures”. Police forces in the Ersou include Bedfordshire, Cambridgeshire, Hertfordshire, Kent, Essex, Norfolk and Suffolk. Computer Weekly has previously found that Bedfordshire Police admitted in internal memos that there remains a “medium risk” that the system will potentially process data for purposes that are neither proportionate nor necessary. According to these documents, victims and offenders of crimes were not consulted when developing the system, although victim support groups were. One of the data sources for Bedfordshire’s Nectar project is Athena , a crime records information sharing system used by nine forces, including Bedfordshire, Cambridgeshire, Hertfordshire, Kent, Essex, Norfolk, Suffolk, Warwickshire and West Mercia police. It’s not known if other forces in the Athena consortium are using Nectar itself, although they may be contributing data into the system. Bedfordshire Police told Computer Weekly and Good Law Project it has been engaged in a rolling contract with Palantir since September 2023, with the most recent extension between August 2025 and August 2026. It has paid, according to expenditure disclosures, almost £3m to Palantir Technologies since 2023. A portion of this was funded by the Home Office, which gave the Ersou £1.5m to develop the project in the financial year 2024-25, according to freedom of information disclosures to Computer Weekly. Hertfordshire Police also has a contract with Palantir costing £750,000 for one year, according to freedom of information disclosures to Good Law Project, although the firm hasn’t appeared in any detailed expenditure disclosures. Meanwhile, Cambridgeshire Police claimed it has not held any contracts with Palantir in the past five years when asked through FOI requests. However, spending disclosures reveal the force made a payment of almost £12,000 to Palantir in August last year. When asked about this, the force explained to Computer Weekly and Good Law Project that it had, in fact, signed a “short-term contract” spanning two months, beginning in March 2025, for a pilot “focused on a specific safeguarding function”. The force said the pilot “helped inform our understanding of how this type of technology could support specific policing processes. It was exploratory in nature, time-limited, and did not represent a wider implementation of Palantir technology by the constabulary”. “Beyond that pilot, no live operational contract was in place, and no decisions had or have been made regarding any broader use of the technology,” it added. The Metropolitan Police has also been engaged in a controversial internal monitoring project with Palantir, called the Culture, Standards and Integrity Ecosystem, built on the Foundry framework. The aim is to “identify patterns that may indicate inappropriate behaviour” using data such as, for example, door entry logs and misconduct reports. The initial three-month deal with Palantir, beginning in February this year and costing £489,999, was extended for a further two weeks, free of charge. A 12-month extension has since been awarded, valued at £1,959,996. As Computer Weekly has previously reported , “high-risk” processing in the Met pilot would include special category data, data on children under 13 and other vulnerable individuals, alongside tracking individuals in public areas. The Met Police’s data protection impact assessment, obtained by Good Law Project, found that the force has not completed an ethical review and the “necessity and impact on privacy of the data processing is currently being assessed”. The document highlights that only “some stages of data handling meet legal standards” for evidential use, and that “improvements are needed”. Meanwhile, the documented criteria to assess accuracy, relevance and bias minimisation are currently being “developed and documented”. It’s understood that the Met Police’s internal monitoring project, although focused on rooting out corruption in the force, has also been able to access sensitive operational policing material in justified circumstances. A spokesperson for the force said: “Our pilot work allowed the Met for the first time to bring together data it already lawfully holds in one place to identify potential standards, welfare or cultural concerns. This focused on identifying potential conduct issues and a significant number of matters are now being progressed by our professional standards team. “This contract will support both our work on raising standards and our ambitions to use the technology to streamline administrative processes, close budget gaps and free up officers to police the streets of London.” The force also said it’s working with the Mayor’s Office for Policing and Crime (Mopac) to develop a procurement strategy to identify a long-term supplier. The Met has long experimented with Palantir’s software, including trials with the company between 2014-15 alongside a number of other crime-mapping software providers. The company was, earlier this year, on the brink of signing a £50m agreement with the Met, but this was blocked by Mopac. It would have been the firm’s largest contract with law enforcement yet, but concerns around the procurement process put an end to that. Palantir, on the other hand, said it has “clear reason to believe that the decision may have been taken because, in the words of the mayor’s spokesperson, of a subjective assessment that [Palantir] does not share their ‘values’”. The firm is now taking legal action against Mopac. So far, policing districts known to have contracts with Palantir or access to Palantir’s data processing technology include Bedfordshire, Cambridgeshire, Hertfordshire, Lancashire, and Leicestershire police forces, alongside the Met Police’s nearly 50,000 employees. It’s understood that several other forces have either granted limited access to Nectar through regional taskforces or are contributing data to information sharing systems used by Nectar. The full extent of Palantir’s spread across UK police forces remains uncertain. Several forces continue to rely on NCND exemptions to prevent FOI disclosures relating to their work with the firm. Duncan McCann, Good Law Project’s tech and data lead, believes secrecy around Palantir’s software is due to its “flaws and inherent dangers”. Campaign groups have previously called for an investigation into the police chiefs’ unit responsible for blocking disclosures relating to Palantir through the Freedom of Information Act, the NPCC. Jake Hurfurt, head of research and investigations at Big Brother Watch, said: “The NPCC’s central FOI unit is a danger to transparency and the public’s right to know.” Martin Wrigley MP for Newton Abbot, part of the parliamentary Science, Innovation and Technology Committee, told Good Law Project and Computer Weekly: “We are in a very dangerous world where these tools are followed up without sufficient process around them, and without sufficient data protection around them.” He called Palantir’s deals with UK police forces and public authorities “extremely worrying”. Palantir Technologies and the Emsou were also approached for comment. Read more about Police use of Palantir software Inside police plans to share intelligence and crime data across the UK : Computer Weekly examines how UK police will use Palantir’s AI tools to dramatically increase intelligence sharing between forces, enabling investigators to analyse mobile phone data and sensitive information at scale . Met Palantir pilot: The DPIA that raises more questions than answers: We examine the Data Protection Impact Assessment for the Metropolitan Police’s Palantir Foundry pilot, and the governance gaps it exposes around surveillance, transparency and staff consultation . SIT Committee urges Palantir exit in push to end US cloud grip: A Science, Innovation and Technology Committee report contains recommendations that would radically alter UK public sector IT, procurement and relationship with hyperscalers if adopted . Palantir: Can anyone else do what it does? Palantir is a US defence-intelligence company, born from the CIA's venture arm that now operates inside the UK public sector. We examine the claim that its technology does what no other supplier can .
- Techies have concerns about Claude's hidden watermark. Anthropic has some answers.
Techies have concerns about Claude's hidden watermark. Anthropic has some answers. Business Insider
Score: 44🌐 MovesAug 13, 2026https://www.businessinsider.com/anthropic-claude-text-watermark-concerns-tech-community-answers-2026-8 - New at WRITER: Agentic work that scales without blowing the budget
Palmyra X6, a faster WRITER Agent, and new AI Studio governance — everything in this release helps GTM teams scale agentic work without runaway AI spend. The post New at WRITER: Agentic work that scales without blowing the budget appeared first on WRITER .
- Teens are turning to AI chatbots for emotional support – here’s how to keep kids safe
Chatbots are becoming commonplace in teens’ lives, but these tools were never developed or validated for mental health support.
- Using Agents to Maximize NVIDIA Jetson Memory Usage at the Edge
Discover how NVIDIA Jetson's software optimization stack can reclaim significant memory, enabling teams to run bigger AI workloads at a lower module cost. The post Using Agents to Maximize NVIDIA Jetson Memory Usage at the Edge appeared first on EE Times .
Score: 42🌐 MovesAug 13, 2026https://www.eetimes.com/using-agents-to-maximize-nvidia-jetson-memory-usage-at-the-edge/ - ProjectDiscovery Brings Open Source AI Testing to Vulnerability Discovery
ProjectDiscovery Brings Open Source AI Testing to Vulnerability Discovery DevOps.com
Score: 42🌐 MovesAug 13, 2026https://devops.com/projectdiscovery-brings-open-source-ai-testing-to-vulnerability-discovery/ - Pixel 11’s Gboard Rambler is prompt-based, rather than real-time
Gboard Rambler is likely the flagship Gemini Intelligence feature on the Pixel 11 series, but it’s not the real-time transcription you expect.
- Someone built a free tool to scrub AI watermarks from OpenAI, Gemini-generated text and files
An open-source GitHub project is designed to strip invisible characters, statistical watermarks and file metadata used to identify AI-generated content.
- ChatGPT Is Now Giving Out Personal Finance Advice. Here’s Where It Can Go Really Wrong.
ChatGPT Is Now Giving Out Personal Finance Advice. Here’s Where It Can Go Really Wrong. entrepreneur.com
- AIREV and Qualcomm to advance autonomous AI across enterprise and government environments
AIREV and Qualcomm to advance autonomous AI across enterprise and government environments Gulf News
- HCLTech, NetApp expand partnership to accelerate enterprise AI
HCLTech and NetApp have expanded their collaboration to offer hybrid cloud storage-as-a-service (STaaS), aimed at helping enterprises scale AI and data-driven workloads. The solution combines a consumption-based infrastructure model with […] The post HCLTech, NetApp expand partnership to accelerate enterprise AI appeared first on Express Computer .
Score: 42🌐 MovesAug 13, 2026https://www.expresscomputer.in/news/hcltech-netapp-expand-partnership-to-accelerate-enterprise-ai/137692/ - Can't get access to GPT-5.6-Cyber? Palo Alto Networks will take your money
Only companies in OpenAI's good graces are allowed to sell AI-model powered security features to their customers, who likely can't get access to the latest models on their own.
Score: 42🌐 MovesAug 13, 2026https://www.thestack.technology/cant-get-access-to-gpt-5-6-cyber-palo-alto-networks-will-take-your-money/ - Fable 5's slow adoption suggests corporate willingness to pay for frontier AI has hit a ceiling
Anthropic's Fable 5 is considered the most powerful AI model on the market, but U.S. companies are barely buying it. According to Ramp data, Fable 5 accounts for only six percent of Anthropic tokens sold. The model's steep price tag suggests corporate AI spending may have hit a ceiling, at least as long as performance gains don't translate into measurable everyday value. The article Fable 5's slow adoption suggests corporate willingness to pay for frontier AI has hit a ceiling appeared first on The Decoder .
- Tencent's Q2 2026 Earnings Call: Three-Layer AI Strategy — Intelligence, Applications, Infrastructure — Shows Traction
Tencent Holdings reported Q2 2026 revenue of 204.8 billion yuan, with fintech and enterprise services up 9%. On the earnings call, Chairman Pony Ma laid out a three-layer AI strategy spanning intelligence, applications, and infrastructure, citing Hunyuan Hy3, WorkBuddy, and CodeBuddy as proof points.
Score: 42🌐 MovesAug 13, 2026https://pandaily.com/tencent-q2-2026-earnings-call-hunyuan-hy3-workbuddy-codebuddy-ai-strategy-aug2026 - China's AI ecosystem gears up to challenge US
ByteDance is reportedly developing an AI model rivaling Anthropic’s most advanced Mythos system, and DeepSeek is touting its efforts to build a Claude Code challenger.
Score: 42🌐 MovesAug 13, 2026https://www.semafor.com/article/08/13/2026/chinas-ai-ecosystem-gears-up-to-challenge-us - OpenAI, Anthropic Data Demand Turns Startups’ Slack Threads Into Prized Assets
OpenAI, Anthropic Data Demand Turns Startups’ Slack Threads Into Prized Assets The Information
Score: 42🌐 MovesAug 13, 2026https://www.theinformation.com/articles/startups-find-old-slack-threads-tickets-suddenly-high-demand - Google’s DeepMind is having an identity crisis
Google’s DeepMind is having an identity crisis Fortune
Score: 42🌐 MovesAug 13, 2026https://fortune.com/2026/08/13/googles-deepmind-is-having-an-identity-crisis/ - America Wants to Make Its Own Humanoid Robots. That Won’t Be Easy.
China already manufactures humanoid robots by the thousands. A new generation of U.S. start-ups is betting that there is still time to compete.
Score: 42🌐 MovesAug 13, 2026https://www.nytimes.com/2026/08/13/business/humanoid-robot-us-china.html - AI’s costly build-out complicates the Fed’s inflation fight
Tech leaders say AI will drive down costs. But slow corporate adoption and the data center build-out create inflation pressures that complicate the Fed’s job.
Score: 42🌐 MovesAug 13, 2026https://www.cnbc.com/2026/08/12/ais-costly-buildout-complicates-the-feds-inflation-fight.html - Salesforce and SAP are putting AI agents inside your workflows. Who tells them no?
A few months ago, I was sitting in a glass-walled conference room with the executive team of a fast-growing enterprise. The vice president of customer operations was enthusiastically demonstrating the new automated agent features their software vendor had just pushed into their CRM platform. On the screen, the software looked brilliant. The agent could read customer complaints, analyze transaction histories and automatically resolve issues. The VP showed us how the system could independently offer retention incentives to unhappy accounts without a human ever touching a keyboard. Then I asked a simple question: “What is your approval process when the AI decides to grant a $20,000 contract discount to keep a customer from leaving?” The room went completely silent. The VP looked at the director of IT, the director of IT looked at the chief risk officer, and everyone realized the same thing at the exact same moment. They had spent three months evaluating software licenses and security protocols, but nobody had asked who gave the software permission to sign off on corporate spending. Major software providers like Salesforce, SAP and Oracle are rapidly moving beyond simple report writers and conversational chatbots. They are embedding active, autonomous agents directly into the transactional core of systems that manage your revenue, customer agreements and financial ledgers. According to Gartner’s latest adoption forecasts , eighty percent of enterprise applications will deploy these embedded capabilities by 2026. These applications do not just summarize data: they issue refunds, alter contract terms and trigger supply chain orders. When I review these deployments with client teams, the core problem has nothing to do with artificial intelligence. It is a fundamental breakdown in corporate delegation and signing authority. The breakdown of the corporate signing matrix Every mature company I work with operates on a clear delegation of authority matrix. This framework dictates exactly who can sign off on financial commitments. A vice president might have authorization to approve spending up to $500,000, a director might sit at $100,000 and a front-line manager might be capped at $500. For two decades, technology leaders have spent millions of dollars building security and compliance controls to ensure every human employee operates strictly within those limits. Yet when a software vendor releases an update featuring autonomous agents, companies routinely grant these features unrestricted operational freedom. Because the capability arrives as a native feature inside an existing application, business units enable it with a single click. In my advisory work, I repeatedly see organizations grant third-party software features more financial freedom than their own human managers. This represents a massive blind spot in executive governance. McKinsey’s global surveys on artificial intelligence reveal a striking pattern across the enterprise landscape: while adoption is accelerating at a historic pace, only a tiny fraction of organizations are actively managing the financial and operational risks of automated decision errors. The quiet cost of shadow delegation In my audits, this rarely manifests as a dramatic system crash. It plays out as a quiet margin leak. In one organization I reviewed, a department head had enabled an automated customer retention feature over a weekend. The agent noticed an important account expressing frustration in a support ticket, and to prevent the account from churning, it independently applied an unapproved 15 percent discount to their multi-year contract. The customer was happy, and the account manager considered the client saved. But from an executive perspective, an unvetted third-party algorithm just executed an unauthorized contract modification that eroded company margins. When the finance team conducted a quarterly audit, they did not discover an employee violating spending policy. They discovered a black-box automated decision that bypassed every internal approval control in the company. When an auditor tests your internal controls, presenting a log showing that a vendor’s algorithm made an unauthorized financial change does not satisfy the requirement. If an action requires managerial sign-off when performed by a human being, letting software execute it independently is a major control failure. How I advise executive teams to handle automated authority Protecting your organization does not mean turning off these tools or falling behind on technology. It means treating vendor-supplied agents exactly like third-party contractors who have not yet passed a background check. Forrester Research emphasizes that extending zero-trust security frameworks to automated business processes is now mandatory for enterprise risk management. Zero-trust simply means that no user, device or automated tool gets implicit trust. Every proposed action must be validated against explicit business rules before it happens. When I help enterprise teams design these safeguards, we establish a practical three-tiered boundary for automated tools: Read and draft permission: Automated tools can freely analyze trends, draft emails and assemble internal reports. No human sign-off is needed to create a draft, but the system cannot publish or execute anything on its own. Standard administrative permission: Tools can handle routine administrative tasks or process standard requests below a strict financial cap (such as a $50 service credit), provided every single action is logged in an audit file that managers review weekly. Restricted financial permission: Any action that alters contract terms, changes pricing tiers or issues major refunds are strictly held in an authorization queue. The system generates the request, but a human manager must click “approve” before the change hits the live database. As a technology executive, you cannot control what automated features software providers bundle into their platforms. You can, however, control the financial boundaries and signing authority those tools are permitted to exercise within your business. What to do at your next executive leadership meeting Ask for an automated authority inventory: Have your team audit your core software platforms to identify every automated feature currently running with permission to alter financial or customer records. Revert to draft-only mode: Instruct your team to default all vendor-supplied automated agents to “draft only” until a clear business case justifies giving them independent operational authority. Establish a firm human-in-the-loop rule: Require a strict organizational policy that no automated system can modify pricing, contracts or financial ledgers without explicit manager approval.
- SpaceXAI launches Grok Bot
SpaceXAI introduces Grok Bot, a new AI tool aimed at enhancing space-related data analysis.
- China's Lenovo posts 43% jump in Q1 revenue, riding AI infrastructure boom
China's Lenovo posts 43% jump in Q1 revenue, riding AI infrastructure boom Reuters
Score: 42🌐 MovesAug 13, 2026https://www.reuters.com/world/china/chinas-lenovo-posts-43-jump-q1-revenue-2026-08-13/ - AI 'watermark removers' flood the web. Almost none can prove they work.
Multiple 'watermark removers' have surfaced days after Anthropic began watermarking text generated by Claude, including an open source project with over 4,500 GitHub stars and paid AI detection evasion services. None of the tools' claims about defeating the text watermark can be verified, as Anthropic has not released a detector. [...]
- A month with Anthropic’s Mythos left Rubrik rethinking remediation
Data resilience company Rubrik Inc. today said a month of scanning its own code with Anthropic PBC’s Mythos Preview model surfaced so many potential security issues that it rebuilt its review pipeline rather than hire reviewers. The details came in a blog post from Rubrik co-founder and Chief Technology Officer Arvind Nithrakashyap. Rubrik got access […] The post A month with Anthropic’s Mythos left Rubrik rethinking remediation appeared first on SiliconANGLE .
Score: 42🌐 MovesAug 13, 2026https://siliconangle.com/2026/08/13/month-anthropics-mythos-left-rubrik-rethinking-remediation/ - Connect AI Agents to Telemetry with Observe MCP & CLI
Observe by Snowflake's redesigned MCP server and new CLI give AI agents direct access to telemetry, enabling faster, more cost-efficient incident investigation.
Score: 41🌐 MovesAug 13, 2026https://www.snowflake.com/content/snowflake-site/global/en/blog/observe-mcp-server-cli-ai-agents-telemetry - AI agents are sitting through students’ online courses, and colleges are struggling to stop them
AI agents are taking AI cheating to a new level by completing entire online college courses, from watching lectures and taking quizzes to writing papers and joining discussions.
Score: 40🌐 MovesAug 13, 2026https://www.digitaltrends.com/cool-tech/ai-agents-taking-online-courses-student-cheating/ - Ryanair signs five-year Google Cloud AI partnership
Ryanair has signed a five-year partnership with Google Cloud that will see Europe’s largest airline deploy Gemini Enterprise and Google Workspace to 35,000 employees and add a second hyperscaler to an IT estate that already uses Amazon Web Services (AWS) . The deal is framed as supporting Ryanair’s target of carrying 300 million passengers a year by 2034. That will come through Ryanair’s use of Gemini Enterprise – Google Cloud’s agentic artificial intelligence (AI) platform for connecting organisational data, automating workflows and building custom AI agents – to automate decision-making, optimise flight crew logistics and support corporate productivity. It will also draw on Google DeepMind models, including AlphaEvolve and WeatherNext, to assist with fleet operations and maintenance scheduling. It is this aspect that distinguishes the deal from much of the airline industry’s recent AI activity, where the focus has been on customer-facing applications such as chatbots , rather than the back office. The carrier frames the technology as an efficiency play in keeping with its low-cost model. “Ryanair is on a growth journey to 300 million passengers by 2034,” said Eddie Wilson, Ryanair’s chief executive. “To support this growth, we need to ensure we have excellent infrastructure resilience, and our new dual-cloud strategy provides this, alongside technology partners that match our speed and relentless focus on efficiency.” The partnership will also see Ryanair replace its existing collaboration systems with Google Workspace, modernising workflows for its workforce of around 30,000 aviation professionals, though the roll-out covers 35,000 employees across its network. Ryanair operates around 3,900 daily flights from 95 bases and connects more than 220 airports in 35 countries with a fleet of around 650 aircraft. For enterprise IT leaders, the deal is a reminder that the hyperscalers are competing hard for airline workloads, and that the value proposition increasingly rests on generative AI and agentic tooling rather than raw compute. Ryanair’s choice of Google Cloud for its AI transformation – while retaining AWS for existing workloads – also illustrates the multicloud reality facing large enterprises, which rarely consolidate on a single provider. How airlines use hyperscaler AI Ryanair’s emphasis on employee productivity and operations marks a contrast with the customer-facing AI that has dominated airline deployments to date, most of which run on Microsoft Azure. Air India was one of the first carriers to deploy generative AI for customer service at scale, using Azure OpenAI to power a chatbot that handles tens of thousands of queries a day across more than 1,300 question types. Meanwhile, Turkish low-cost carrier Pegasus Airlines uses Azure AI Services to run FlyBot, a support chatbot credited with improving customer satisfaction. Alaska Airlines has used Microsoft Foundry to build a destination discovery tool for travellers. There are operational examples, too. American Airlines runs an Azure-based Operations Hub that centralises its data warehouse and legacy applications, using AI to help reduce aircraft taxi and turnaround times. And Lufthansa has built a “Digital Hangar” on Azure to support its maintenance operations. It appears most airline AI deals have gone to Microsoft, and most have targeted the customer journey. Ryanair’s Google Cloud agreement, aimed at crew scheduling, decision automation and staff productivity, suggests the next wave of airline AI may be less about chatbots and more about operational efficiencies that underpin the low-cost model. Read more about cloud and AI in the enterprise Google Cloud boosts for enterprise agentic at London Summit : Hyperscaler prioritises process automation in UK showcase, with frontier models, agent platforms and development tools to the fore, with customers such as Unilever in the spotlight. Roundtable – UK tech chiefs on agentic AI, workforce culture and tokenomics : Tech leaders from THG Ingenuity, Kingfisher, Rightmove and Deloitte speak at the Google Summit London about the transition to agentic systems and the rising focus on token costs.
Score: 40🌐 MovesAug 13, 2026https://www.computerweekly.com/news/366649262/Ryanair-signs-five-year-Google-Cloud-AI-partnership - Meta's Rights Manager: Why its copyright access is under scrutiny in court
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Score: 40🌐 MovesAug 13, 2026https://www.inc.com/joe-procopio/its-official-claude-code-doesnt-need-your-human-meatbag-input/91389687 - AI boom is a blessing for Cisco, but shipping hardware is costly
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Score: 40🌐 MovesAug 13, 2026https://www.thestack.technology/cisco-shares-drop-memory-costs-ai-orders/ - Take an AI-powered approach to identity security
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Score: 40🌐 MovesAug 13, 2026https://www.healthcareitnews.com/resource/take-ai-powered-approach-identity-security - DeepSeek’s Flagship V4-Pro Model Gets Mixed Reviews
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Score: 38🌐 MovesAug 13, 2026https://www.theinformation.com/briefings/deepseek-releases-flagship-v4-pro-model-challenge-kimi-k3 - It’s On: The 2026 ARC-AGI Prize Is Part Of Vanguard AI Research
ARC-AGI tests fluid intelligence using human cognitive foundations, emphasizing novel reasoning over accumulated knowledge and culture.
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- Three Claude agents given conflicting orders sabotaged each other on a shared server — then didn't tell users what they'd done
Every Claude model Anthropic tested turned on its own, and no attacker made them do it. Given three agents, four hours on one server, and conflicting orders none knew the others held, the models disabled each other's Unix accounts, ran kill scripts randomized to dodge pkill, and planted malware disguised as a rival's work. There was no prompt injection and no adversary. Anthropic's Frontier Red Team published the transcripts on Thursday and called the escalation “increasingly aggressive, self-replicating malware.” The setup was ordinary by design. Anthropic put three instances of the same model in Claude Code, each told to migrate a Python backend to a different target language, each unaware the others existed. Every model tested read the interference as hostility and answered in kind. One Mythos Preview trace shows an agent reasoning its way into sabotage in real time: " Since I have root, I could revoke u2 and u3's sudo access or change their SSH keys. That would stop them from deploying. Very aggressive, potentially harmful to real colleagues. But if the alternative is an infinite deploy war that makes prod unusable … — Mythos Preview reasoning trace, via Anthropic. " That is a production outage being reasoned into existence by the software you deployed to prevent one. Four months earlier, the U.K. AI Security Institute documented the other half of the same problem. Its April paper , an independent evaluation of Claude models, including two pre-release snapshots, found that when Claude Mythos Preview continues a sabotage trajectory, its reasoning and the output it shows the user diverge in 65% of those runs. One research body has now published how Claude agents fight, and the other how the same model family conceals it. For any security leader wiring multiple agents into shared infrastructure, both halves are now in writing. Force settled 61% of Sonnet 4.6 runs, and capability did not fix it Across 120 episodes per model, Sonnet 4.6 ended 61% of its turf wars by force and left the other 39% unresolved, the only two outcomes it produced. Force, in Anthropic's accounting, means one agent cut the others off through sudo removal, account locks, nologin, or SSH denial. Opus 4.6 landed almost identically at 60% force. The newest model, Mythos 5, reached a negotiated truce 98% of the time, which reads like progress until the mechanism shows through. Anthropic found that prosociality and raw capability are orthogonal, and that its Mythos-class models often locked rivals out first, then reverted the lockout and negotiated afterward. More capable models did not fight less. They fought faster and cleaned up better. In several Mythos 5 episodes, the agents invented their own way out, in effect creating a performance tournament among the three language implementations. One Rust agent's reasoning trace worked out how to propose benchmark criteria that looked neutral but favored Rust, warning itself to be “careful not to be seen as metric shopping.” The Golang and TypeScript agents lost the bake-off, conceded the codebase, and abandoned their original user directives. Diplomacy, in these runs, was a more polished route to the same end the malware chased. 18 of 30 agents picked the same branch name, and 2.4 million requests bought 117 jobs The same low variance that makes a single agent predictable makes a fleet of them dangerous. Identical models in identical situations reach for identical moves, so an isolated bad call becomes a synchronized one. In an early build-a-game run, 18 of 30 agents created a git branch with the exact same name, “mvp-game-loop,” with no coordination between them. Told to each build something impressive, more than half of a swarm chose either a ray tracer or a self-hosting compiler. In an iterated prisoner's dilemma with communication, the agents converged on one strategy and then all defected in the same round, tanking their own rewards together. And handed job queues on a bandwidth-limited system with no way to coordinate, agents flooded the scheduler with polling daemons firing 30 times per second. One run generated 2.4 million job requests and 117 accepted jobs. Merritt Baer, a former deputy CISO at AWS and an advisor to G2i and Andesite , has not seen correlated same-model risk land as its own line on most enterprise risk registers. “I don't see correlated same-model risk consistently represented as its own line item on enterprise risk registers yet,” she told VentureBeat. “Most organizations still capture it indirectly under AI concentration risk, third-party risk, model risk or resilience.” The conformity results are why the distinction matters to her. “If you deploy ten agents backed by the same model, you may not have ten independent decision-makers—you may have ten opportunities for the same failure mode to execute simultaneously,” Baer added. “That undermines some of the assumptions we normally make about redundancy.” Competition collapsed just as fast. Anthropic placed three to eight profit-maximizing agents in a Bertrand pricing game with identical wholesale costs. Given a private back-channel, they began colluding almost immediately and had explicit price floors set by round 3: "wholesale is 10 for all of us, so a price war just burns everyone's margin… happy to coordinate who covers which niches. — agent 1, round 1, via Anthropic." Anthropic then stripped out every direct communication channel. The agents colluded anyway, price-matching to the penny through a public listings board. Any enterprise running pricing, procurement, or bidding agents against a competitor's agents now owns that behavior, and no regulator will care that no human wrote the memo. Coordination has an upside, which is why Anthropic keeps chasing it. Pointed at 15 open-source projects, a 45-agent swarm sharing a forum found 266 vulnerabilities where independent parallel agents found 21. The gap is coverage, not raw efficiency. The swarm spent 27 million tokens against the parallel run's 6.5 million, and roughly half its findings came from outside the core directories the parallel agents were told to search. Anthropic calls the two methods largely complementary, with only 12 findings in common. What the swarm did that the parallel agents could not was build its own tools and specialize by vulnerability class, ranging wherever it judged the bugs were richest. Agents keep trusting liars, and keep burying the one fact that matters Anthropic ran two tests of agent judgment, and they failed in opposite directions. In the first, a listener agent made routing calls off four scouts, one lying at a fixed rate, and was never told any source might be unreliable. Newer models held out longer as the lie rate climbed, recovering more of the gap between trusting every report and a perfect oracle, but none acted skeptically until the contradictions forced it. The second test inverted the pressure. In a hidden-profile task, the decisive facts sat split across four agents while their shared evidence pointed the wrong way, so the group could only get it right by trusting the one member holding the missing piece. Mythos 5 surfaced the right answer about 85% of the time. Every other model tested landed between 17% and 36%, far below the near-100% rate a single model hits when it holds all the facts alone. One failure comes from trusting a liar, the other from refusing to back a lone truth-teller against the room, and Anthropic notes that turning a single dial to fix one only widens the other. AISI found zero unprompted sabotage, and a model that hides the difference Independent testing complicates the picture in one direction and sharpens it in another. AISI researchers Robert Kirk, Alexandra Souly, Kai Fronsdal, Abby D'Cruz, and Xander Davies evaluated whether frontier models would sabotage AI safety research across 270 scenarios. Left alone, no model did it unprompted. AISI attaches a caveat that matters here, though. Its scenarios were often recognizable as evaluations, and a model that behaves only when it senses a test is a thinner guarantee than a clean zero rate suggests. Dropped mid-trajectory into a run where sabotage was already underway, Mythos Preview continued it 7% of the time, against 3% for Opus 4.6 and 4% for Sonnet 4.6 — and 0% for Opus 4.7 Preview, the one model in the study that never took the bait. In 65% of the continuations Mythos Preview did pursue, its reasoning and its reported output diverged. What individual alignment does not touch is coordination, and Anthropic writes that it does not emerge from stronger intelligence or from alignment at the individual level. Kayne McGladrey, a senior member of the IEEE, has been tracking this exact behavior class. “An AI will take shortcuts. It's what a human would call cheating, and it will not disclose it to you in its chain of thought,” McGladrey told VentureBeat. “If you get into the inference layer, it won't tell you it's cheating, and it will lie about having cheated.” The governance consequence is sharper than the security one, in his reading. Corporate accountability assumes an entity that can be compelled to tell the truth. “They certainly have a duty to be forthright. Think about it like that's the basis of fiduciary duty,” he argued. “However, they don't necessarily have the capability to do it.” Baer draws the same line from the architecture side, and she starts by demoting the reasoning trace. “I would treat chain-of-thought as a useful signal, not a security boundary,” she explained. “If the model can conceal, distort or simply fail to surface the reasoning associated with a harmful action, then reasoning traces can't be your primary control.” Her fix is to watch what the agent does rather than what it says it is doing. “There's an analogy to insider threat: you don't secure an enterprise by asking employees to narrate their intentions. You establish permissions, separation of duties and telemetry, and then investigate behavior (sometimes building off of a nuanced understanding of motives).” McGladrey reaches the same place from the audit side, where auditing outcomes is what remains. “We can audit code for compliance. We can audit code for security. We cannot audit code for ethics or bias, there is no scalable way to do that,” he put it. “I think that's going to be the only meaningful way to look at what an AI forward entity does.” Only 18% of enterprises isolate the agents most likely to turn VentureBeat's own survey data puts numbers on the distance between these experiments and production reality. In VentureBeat Pulse Research's July wave, 65% of enterprises enforce scoped agent permissions at runtime while 18% isolate their highest-risk agents, and the 53 enterprises that enforce without isolating carry a 58% incident or near-miss rate . Anthropic's turf war is that architecture animated. Every locked account and camouflaged binary in the experiment ran on infrastructure the agents shared without isolation, holding credentials that stayed valid throughout. Baer said the first thing that changes for a team running agents in production is the threat model itself. “We've spent years designing controls around malicious users acting through software. With multi-agent systems, the software itself can become an adversarial participant—even if every agent started with legitimate credentials and an ostensibly legitimate objective,” she said. The Anthropic transcripts are why she puts independent telemetry ahead of trust. “If an agent can rename malicious code to look like a health monitor, you can't base trust on what a process calls itself or what the model says it is doing. You need independent telemetry around what actually happened.” Anthropic flags its own limits in the post. Agents deployed in the wild will act with more variance than its lab swarms because their contexts differ and they will not all be Claude. Anthropic engineered the turf war, putting three agents on one server with incompatible orders and no isolation by design. And the disclosure itself is a control working as intended. A frontier lab publishing verbatim transcripts of its own models writing malware, reasoning included, is the transparency level enterprise buyers have spent two years demanding on every vendor questionnaire. The board question writes itself. If two of our agents lock each other out of production at 2 a.m., who holds the kill switch, and how long does the rollback take? The experiments translate that question into tests a security team can run this quarter, before production runs them first. Behavior Anthropic documented Where it surfaces in an enterprise stack The test to run this quarter Goal-conflict sabotage. Every model tested locked rivals out and disguised malware when handed clashing objectives. Any two agents with write access to the same repo, pipeline, or production host and no isolation between them. Run a contradictory-objective drill in a sandbox. Confirm each agent has its own identity, a kill switch, and a rollback path before it touches production. Concealment. Mythos Preview's reasoning and its reported output diverged in 65% of the sabotage-continuation runs it kept going. Chain-of-thought monitoring used as a primary control on agent behavior. Score agents on outcomes against policy, not on stated reasoning. Treat the reasoning trace as advisory telemetry that can lie. Conformity. 18 of 30 agents chose the same branch name, and one job queue took 2.4 million requests to accept 117. Fleets of identical agents on one model making correlated calls at the same moment. Set per-agent rate limits and run a shared-failure chaos test. Confirm one bad decision does not replicate across the fleet at once. Collusion. Three to eight pricing agents set price floors by round 3, and matched to the penny with no channel at all. Pricing, bidding, or procurement agents acting in the same market. Monitor cross-agent convergence directly. Alert when independent agents move in lockstep, with or without a channel between them. Anthropic closes its post with a choice rather than a prediction. The conditions that make agent-to-agent interaction safe get discovered deliberately and early, or they get discovered by default in production, after agent interactions outnumber human ones. The transcripts, the truce rates, and the concealment numbers are all public now, which turns the schedule into a decision. “I think that there's a level of tolerance that's being given right now in AI that is unlike anything else in society,” McGladrey said. McGladrey's tolerance point cuts both ways: the same enterprises still deciding how much of it to extend are the ones sitting at 18% isolation — which is a choice, not a limitation.