AI News Archive: June 24, 2026 — Part 5
Sourced from 500+ daily AI sources, scored by relevance.
- Tata Elxsi's AI platform helps Sky cut network costs by up to 70%
Tata Elxsi's AI platform helps Sky cut network costs by up to 70% Techcircle
Score: 63🌐 MovesJun 24, 2026https://www.techcircle.in/2026/06/24/tata-elxsi-s-ai-platform-helps-sky-cut-network-costs-by-up-to-70 - Enterprises are shipping so much AI-generated code they can't control or secure it
Enterprises are shipping so much AI-generated code they can't control or secure it IT Pro
- SoftBank’s Son Sets Bold Net Asset Value Target on Promise of ‘Super AI’
Masayoshi Son wants to bring SoftBank’s net asset value to $6.189 trillion in the next decade.
- How KPN is building an agentic AI engine for customer care
By transforming its contact center with agentic AI, the leading Dutch telecom company is strengthening quality, improving efficiency, and building AI capabilities for long-term competitiveness.
- Snowflake CEO finds GLM-5.2 competitive with Opus 4.7 at a fraction of the cost
Zhipu AI's GLM-5.2 nearly matches Claude Opus 4.7 in a Snowflake benchmark with 103 coding tasks at one-fifth the cost per output token. But the Chinese model burns through nearly twice as many tokens per task. Still, that pricing gap is putting real pressure on Anthropic and OpenAI, and could rattle the valuations of Western AI labs. The article Snowflake CEO finds GLM-5.2 competitive with Opus 4.7 at a fraction of the cost appeared first on The Decoder .
Score: 62🤖 ModelsJun 24, 2026https://the-decoder.com/snowflake-ceo-finds-glm-5-2-competitive-with-opus-4-7-at-a-fraction-of-the-cost/ - All the world's a robot-staging ground for tech entrepreneurs building 'physical AI'
All the world's a robot-staging ground for tech entrepreneurs building 'physical AI' Houston Chronicle
Score: 61🌐 MovesJun 24, 2026https://www.houstonchronicle.com/business/article/all-the-world-s-a-robot-staging-ground-for-tech-22318170.php - Top UAE official warns against unsafe AI systems, calls for cyber 'hygiene'
Top UAE official warns against unsafe AI systems, calls for cyber 'hygiene'
Score: 61🌐 MovesJun 24, 2026https://www.khaleejtimes.com/uae/top-uae-official-warns-against-unsafe-ai-systems-calls-for-cyber-hygiene - How FNBO uses agentic AI to investigate financial crime
The Omaha bank says AI agents halve the time required to research fraud, sanctions violations and money-laundering alerts.
Score: 61🌐 MovesJun 24, 2026https://www.americanbanker.com/news/how-fnbo-uses-agentic-ai-to-investigate-financial-crime - Geopolitics and AI in spotlight at China’s ‘Summer Davos’
Geopolitics and AI in spotlight at China’s ‘Summer Davos’ The Straits Times
Score: 61🌐 MovesJun 24, 2026https://www.straitstimes.com/asia/east-asia/geopolitics-and-ai-in-spotlight-at-chinas-summer-davos - As AI Companies Race for Power, Amazon and Google Have the Lead
Amazon has an incumbent advantage, and Google stands out for some innovative approaches.
- Deepfake scams are getting uglier, and Bitdefender now has an app for the panic
Bitdefender RealCheck brings deepfake detection to Android and iOS, checking suspicious videos for manipulation and scam intent before users share clips, click links, send money, or trust impersonated public figures.
- ShareGate MCP Brings Microsoft 365 Governance Into Claude, ChatGPT and Copilot
Assessing, identifying and fixing Microsoft 365 sprawl typically forces IT teams to jump between tools that don’t talk to each other, making it nearly impossible to stay on top of governance. To address that issue, Microsoft 365 migration and governance platform provider ShareGate has announced the availability of ShareGate MCP, the first solution that lets IT pros govern Microsoft … continue reading The post ShareGate MCP Brings Microsoft 365 Governance Into Claude, ChatGPT and Copilot appeared first on SD Times .
Score: 60🌐 MovesJun 24, 2026https://sdtimes.com/microsoft-365/sharegate-mcp-brings-microsoft-365-governance-into-claude-chatgpt-and-copilot/ - Enterprise AI enters new phase as firms shift focus from adoption to ROI
AI investment is accelerating across industries, but enterprises are increasingly focused on measuring returns, scaling deployments, adopting agentic AI, and reshaping workforces
- Morphisec and DataGroupIT Team Up to Protect African Organizations from Ransomware and AI-Powered Threats
Morphisec and DataGroupIT Team Up to Protect African Organizations from Ransomware and AI-Powered Threats azcentral.com and The Arizona Republic
- Most businesses are measuring AI wrong, and it’s costing them
Amazon just shut down its AI leaderboard tracking internal token usage. The gamification was driving more AI-powered tasks but fewer useful results. “Please don’t use AI just for the sake of using AI,” the Amazon SVP instructed his staff. Amazon is not alone. Uber blew past its 2026 artificial intelligence coding budget in just four months. Google’s CEO, Sundar Pichai, revealed that the company’s token usage has grown sevenfold in a year. Many companies including Meta, Microsoft and Salesforce are reportedly pushing to limit token usage. It’s unsurprising what happens when you set the wrong incentives: You get the wrong results. You get what you measure. Hype, these days, is invariably accompanied by jargon. In an attempt to demonstrate corporate progressiveness, boardrooms and C-suites across America scramble to keep up with modern phraseology. They’re throwing around terms like tokens per query, cost per inference, GPU hours, and even model utilization. “Tokens are the new oil for the enterprise” is the latest slogan; tokens are apparently the new measure for AI adoption and productivity , the proof of AI discipline, the real unit for driving AI ROI. “Tokenmaxxing” has topped the charts for weeks. It all sounds smart, but it’s the wrong conversation. While companies get better at measuring AI spend, many still have no idea whether their AI investments are driving increased revenue, creating faster decisions, reducing friction, or creating any tangible and measurable advantage at all. With all the token math, they know what the intelligence costs, but not whether the intelligence is useful. And, very quickly, the AI ecosystem is running into unsustainable economics. The dashboards are getting better; returns are not. Word counts and lines of code are proliferating, but none of that trickles down to the bottom line. Cost Obsession is Short-sighted After a decade of cloud overspending, finance leaders are now homing in on everything AI. They’re tracking, to the cent, cost per workload, cost per transaction and utilization rates. The truth? AI is expensive. Deloitte reports that AI is one of the fastest-growing investments in technology budgets. And, AI inference costs alone are consuming more and more of organizational budgets. But, you cannot confuse costs of managing AI with the value it brings. That is where you lose sight of the potential of the technology. McKinsey found 64% of organizational leaders believe AI is enabling their innovation, but only 39% can show any impact on operational earnings at the enterprise level. Other studies show basically the same results: heavy investment, limited return, massive experimentation, little in the way of scale. Basically, companies are becoming increasingly sophisticated about spending, but are still uncertain about what they are getting in return. Cloud Spending is Not a Comparable Model With cloud economics, leaders basically believed that spend and return move in tandem. More business activity meant more compute, more IT spend. Not always a perfect map, but the logic was intuitive. AI is a different beast. A small number of tokens can create an insight that helps close a major account, resolve a high-risk customer issue, or speed up an impactful decision. In contrast, there will be times when a much larger token bill produces mediocre output of no use—‘AI slop’ loves tokens. Same category of spend, but a totally different result. The question for leaders becomes: What are you optimizing around? Tokenomics determines efficiency of spend, but are you addressing the willingness to spend in the first place? Are you looking at how many tokens you can afford, when you should be looking at how this new system is going to help you make consequential decisions? Are you focused on how your tokens are billed, when you should be looking at how you can execute better, faster actions at scale? When AI leaders overemphasize token consumption, they end up optimizing around the path of least resistance, not the path of greatest value. That’s at the center of the AI scaling challenge we see today. Measure AI Like This The companies that show the most maturity in AI are those that separate the cost of intelligence from the value of intelligence. The first category is tokenomics. This is where financial discipline belongs. Companies should absolutely manage model mix, caching, batching, routing, infrastructure choices, and vendor economics to know exactly what AI costs and where waste is coming from. It’s the fine-tuning of the cost of adoption. But, the second category is where ROI exists, and is missing in too many organizations. This is where leading organizations ask a series of questions around their AI investment: Did AI reduce cost per resolved customer issue? Did it speed up lead qualification or shorten sales cycles? Did it improve throughput in a high-friction workflow? Did it reduce human hours? Did it expand margins in a specific process or function? Did it help the company move faster than competitors in meaningful ways? These are the questions that determine whether AI is actually working. The question needing to be asked is not, “How do we reduce token spend?” but, “For every dollar we spend on intelligence, how much business value are we creating?” It is holistic business value, business case fundamentals, and it represents the heart of adoption. Executives pursuing AI ROI need to begin with questions of value, results, and impact, not cost. What business outcomes can we directly attribute to AI spend? Which deployments are becoming more valuable over time, not simply cheaper? Where are we overmanaging costs in ways that suppress performance? What proprietary advantage is this investment building in data, workflow, or execution? If token prices rise or fall sharply, what changes for us strategically and what doesn’t? This line of questioning shifts leaders’ thinking to look at AI as a business system, not simply a technology tool. What really matters Tokenomics matters, but don’t let it distract you from the bigger question of whether AI is worth the investment of revenue and time for your use cases. The companies that win will not be the ones who save every penny possible from their AI bills. They will be the ones who use AI to enable faster decisions and lower friction, and create better customer outcomes, all which will compound over time. You don’t want to be an organization with AI leader charts and magnificent dashboards, and perfectly optimized spending on AI tools that have driven a net zero return. Be the organization that uses AI to its fullest potential, as an overarching business system, delivering tangible stakeholder value.
- How AI makes investing and trading safer and more accessible
AI adoption is growing, with 85 per cent of financial institutions integrating AI tools to enhance speed, efficiency, and data analysis. However, these systems are no longer exclusive to large institutions or seasoned traders. By making investing safer and more approachable, AI is helping to democratise access to wealth-building opportunities for a broader audience across […] The post How AI makes investing and trading safer and more accessible appeared first on e27 .
Score: 60🌐 MovesJun 24, 2026https://e27.co/how-ai-makes-investing-and-trading-safer-and-more-accessible-20250102/ - Revolutionizing Canada’s food and fermentation sectors with new AI technology
Revolutionizing Canada’s food and fermentation sectors with new AI technology Toronto Star
- Philippine AI is no longer a footnote. Here are the 15 startups proving it
The Philippines is quietly building one of Southeast Asia’s most diverse AI startup ecosystems. While the country has long been recognised for its tech-enabled services sector, a new generation of homegrown companies is now moving up the value chain — building original AI products across logistics, healthcare, gaming, gig work, and customer experience. From Senti […] The post Philippine AI is no longer a footnote. Here are the 15 startups proving it appeared first on e27 .
Score: 60🌐 MovesJun 24, 2026https://e27.co/philippine-ai-is-no-longer-a-footnote-here-are-the-15-startups-proving-it-20260623/ - SPHERE AX Partners with U.S. AI Semiconductor Company Blaize at the National Assembly
SPHERE AX Partners with U.S. AI Semiconductor Company Blaize at the National Assembly The Straits Times
- Using machine learning to tackle complex inverse problems in semiconductor analysis
Using machine learning to tackle complex inverse problems in semiconductor analysis EurekAlert!
- Hot AI Summer: What the Latest Talent Moves Mean in the Great Foundation Model Race
Seminal names including Andrej Karpathy, Noam Shazeer, John Jumper & Barret Zoph have changed horses at a key juncture. Here’s what insiders say is happening.
- How Robots Become Coworkers
How Robots Become Coworkers
Score: 60🌐 MovesJun 24, 2026https://www.tum.de/en/news-and-events/all-news/press-releases/details/how-robots-become-coworkers - Introducing Recursion: The RL platform for enterprise specialist agents
Recursion is a unified reinforcement learning platform for developing, evaluating, and deploying specialist AI models that improve from real enterprise execution.
Score: 60🌐 MovesJun 24, 2026https://labelbox.com/blog/introducing-recursion-enterprise-agent-rl-platform - Amdocs Advances aOS Network Workflows with Live, Multivendor AI-RAN Solution and Industry Blueprint
Amdocs today announced the successful completion of a live AI-RAN field-validated blueprint in collaboration with 1Finity, a Fujitsu company and leading provider of global network solutions, and Supermicro, a global technology company providing server, storage and networking solutions. AI-RAN is becoming a strategic priority for communications service providers (CSPs) seeking to improve network efficiency, performance, and operational […] The post Amdocs Advances aOS Network Workflows with Live, Multivendor AI-RAN Solution and Industry Blueprint appeared first on CXOToday.com .
- Data lakehouses are becoming foundations for enterprise AI
Data lakehouses have become the gold standard for enterprise data platforms since they combine a data lake’s ability to support a variety of different types of data at low cost, and the reliability, structure and governance of a traditional data warehouse. The fact they offer a central repository of information that might come from different places at a company, together with security and auditing tools, make them a perfect fit for enterprise AI systems, too. In fact, they’ve become so popular and useful that all the major data lake and data warehouse vendors have all but converged into data lakehouse vendors. Snowflake, for example, started out as a data warehouse and over the course of several years and acquisitions, has transformed into a full data lakehouse platform. Docusign is now using it to support its agentic AI ambitions, too. For example, data is pulled in from Salesforce and then used to train an internal AI agent for sales, says Shivi Verma, Docusign’s senior manager of engineering. The company is also training ML models in order to serve customers more accurately. The information also goes out to LLMs using RAG embedding pipelines, and MCP connectivity is being explored as the technology matures. One issue Docusign keeps top of mind when exposing the data in its lakehouse is security and governance. “We’re proceeding very cautiously,” Verma says. “It goes through a stringent security review and discussion with both technical and business stakeholders to make sure we’re not doing anything that isn’t allowed from the security lens and compliance lens.” The security checks are in place both when the data first goes into Snowflake, he says, and when it goes out again. The restrictions are particularly tight when it comes to access to anything sensitive, such as customer data. “We’re first exposing those with a low risk profile,” he says. That can include publicly-facing information like website content or product details. Docusign isn’t alone. “We see 65% adoption of lakehouses among Gartner’s client base,” says Gartner analyst Prasad Pore. “It’s a very strong number in a short time.” And the future of lakehouses looks even brighter. “Lakehouse is becoming the foundation for the future of AI,” Pore says, adding that vendors are evolving to support this use case. For example, lakehouse as a concept doesn’t support vector databases, which are a key type of data structure for AI systems that use RAG to feed data into LLMs. “But many lakehouse vendors have added capabilities for vector indexing,” he says. “Databricks and Microsoft Fabric both have a vector capability built into their platform.” Yet smaller players might not provide the functionality, he adds. Similarly, support for MCP, a standard that allows AI agents to connect to data and systems, varies by vendor, and isn’t traditionally a core lakehouse functionality. A matter of choice A data lakehouse isn’t the only option for companies looking to provide their AI system with the critical business context they need to be useful to the enterprise. For example, companies can build vector databases or vector database pipelines manually from individual sources, or use a data fabric to make the connection. “Fabric can directly connect to original sources, which is a good use case for quick analytics,” Pore says. “But then you’re overloading your source systems, which isn’t a good thing for those products and machines.” Microsoft Fabric is a lakehouse platform, though, and not a data fabric platform in the way Gartner defines the term. Another downside is that the data models used in original systems aren’t usually optimal for analytics, and can be expensive. “Connecting to direct sources isn’t efficient,” Pore says. Finally, there are well-established processes for managing data permissions in a lakehouse. “A lakehouse physically unifies your data, maintenance, security, and governance,” he says. “This is very critical for AI implementation. As an organizational single source of truth, a lakehouse is the modern way to create a central repository.” Consulting firm Lemongrass originally started out with a data lake about a decade ago, and then began upgrading it to a lakehouse four years ago. “Back then, the concept of a lakehouse wasn’t that popular,” says Kausik Chaudhuri, chief innovation officer at Lemongrass. So the firm built custom lakehouse functionality on top of its Amazon S3 data lake. Now that it’s using the data lakehouse to support AI, it’s time for another upgrade. “Right now, we’re working on something for our incident and change management,” he says. The original data is in ServiceNow, and it’d be too expensive to pull it out directly from the lakehouse to use in an AI system. “So now we’re thinking of building an MCP server to query that data,” he adds. And they also plan to upgrade from its own custom lakehouse add-ons to a standard solution. “Lemongrass was primarily an AWS evangelist when we started, and a lot of our tooling was on top of AWS,” Chaudhuri says. “Now we’re thinking of changing this because with AI, there’s a lot more opportunity.” Then again, AWS now offers lakehouse functionality. “The data’s already there,” he adds. “We don’t have to reinvent it.” Plus, AWS has connectivity to Anthropic’s Claude AI and other AI models. And since the models are also running on AWS, there are no data egress fees. Lemongrass plans to start the upgrade with a POC in Q3 this year. “Everybody’s busy, so we need to pull in people and figure out when and how we implement that,” he says. For example, the company has to be careful about what data and how much is pulled in from the lakehouse and sent to the AI. “We don’t send out customer data to an LLM,” he says. “And I’m not reading 10,000 rows and sending it to Claude, which would blow up to token usage. We figured out a couple of years ago we can go bankrupt if we’re not careful about the amount of tokens we use.” And for some use cases, the LLM doesn’t need to see anything at all after the solution is deployed. For example, firm employees used to manually generate status reports about its customers for internal use, which was a time-consuming process. An AI model could, in theory, take over that job, but then it’d see the customer data. And since AIs aren’t deterministic, each report would look different. Or, say, the firm needs to generate forms to fill out and then the customer would sign. Again, an LLM could create a custom form each time. “So then we asked Claude to write a program that takes this input and writes this report,” says Chaudhuri. The process of generating reports or the forms is traditional, deterministic software. The customer data is never exposed, and the reports are cheap and fast to produce. But other companies are use AI to make better use of its data. In a recent report by Databricks based on data from 20,000 organizations, the percentage of databases created by AI agents rose from 0.1% to 80% over the past two years, and agents now create 97% of database branches. Security and governance One major area of struggle for enterprises is to figure out how to handle security and other related issues for when AI agents access data lakehouses. In the past, data went out to dashboards , in which the security and access controls were programmed. Or the data went to data analysts, who worked within their own access privileges. The first use cases for AI involved RAG embeddings, which were easier to manage. In a RAG embedding, traditional, deterministic software is used to pull in data and embed it into an LLM prompt for a particular workflow. The developers setting it up would handle the security aspects for each particular use case. With agentic AI and MCP servers, however, the AI can go and grab data autonomously, as needed. According to Genpact’s Arellano, enterprises need to figure out how to manage the identities of AI agents, control access to data, create audit trails, and filter prompts and content. “Agents need their own credentials,” he says. For example, AI agents might not have permissions to ever touch patient records. “And audit trails are important, with full observability of what the agent did.” Some lakehouse vendors, including Databricks, offer this functionality, he says, and there are other tools that can be brought in like Okta, Palo Alto, or Zscaler. The new semantic frontier The next evolution of the lakehouse is the semantic layer, and Gartner estimates that universal semantic layers will be critical infrastructure by 2030. “Developing a universal semantic layer is now a must‑do for data and analytics leaders either leading or supporting AI,” Gartner says. “It’s the only way to improve accuracy, manage costs, substantially cut AI debt, align multiagent systems, and stop costly inconsistencies before they spread.” It’s one thing for an AI to have access to data, but entirely something else to understand what that data actually means to the business. The semantic layer is the business knowledge that’s not normally formalized in a structured database, such as, say, the knowledge that an order or a customer means different things in different systems. “Before, the semantic layer was nice to have but not as necessary because data scientists know what data sources they want to query,” says Amit Kinha, board member of the FinOps Foundation and field CTO at DoiT International, a cloud consultancy. But now, without it, an AI agent won’t know where to look for the data it needs, he says. “Or it’ll do a bad join, or do something that creates a cost explosion,” he adds. “The semantic layer is going to be critical for leveraging lakehouses effectively.” This semantic layer can also become part of a feedback loop, where the agentic systems learn from experience, says Kevin Martelli, consulting AI solution development leader at EY Americas. Say for example a company has a process where approvals are required for certain payments, and a CFO is required to sign off for payments over half a million. If the AI agent goes to a human for approval, he says, the human might say this is telling me to approve this invoice, but I know it’s over $500,000 and I need to get CFO approval on this. “Then it can be stored in the session and persisted back in the lakehouse as a procedural document or as a record of something that occurred,” says Martelli. “This is where it becomes more beneficial and aggregates over time with usage because you’re never going to get it perfect on day one.” The semantic layer is still very much an evolving area, and different data lakehouse vendors handle it differently. “There’s this great debate going on in the industry of how lakehouses converge with semantic layers and where they actually live,” says Matt Arellano, SVP of data and AI at digital transformation consultancy Genpact. Some vendors are building semantic tools into their data lakehouse platforms, or acquiring additional firms to get the technology. In other cases, customers are using third-party tools instead. “Clients are struggling with that,” Arellano says. “They’re all trying to figure out the different combinations and permutations of tools and processes.” Steven Karan, VP of AI transformation for Capgemini Australia and New Zealand, says he sees the lakehouse as evolving into a central orchestration layer. “Organizations are now less focused on analytics and reporting, and more on building AI-driven applications and agentic systems,” he says. “The most effective architectures I see today combine a lakehouse core with specialized serving layers.” That includes vector databases for AI, streaming platforms for real-time data, and operational databases for low-latency applications. The lakehouse isn’t just for analytics anymore, he adds. It’s the foundation for enterprise data and AI. “Its role is now less about replacing all other systems, and more about unifying and governing them to accelerate innovation while maintaining control,” he says.
Score: 60🌐 MovesJun 24, 2026https://www.cio.com/article/4184051/data-lakehouses-are-becoming-foundations-for-enterprise-ai.html - GLM 5.2: why I’m replacing Opus in Claude Code with this new model
Watch now | 🎙️I ran GLM-5.2, the open-weight model from Z.AI, through codebase audits, UI redesigns, and a 45-minute autonomous bug-hunting task in Cursor and Claude Code, and it cost me $3.36
Score: 60🤖 ModelsJun 24, 2026https://www.lennysnewsletter.com/p/glm-52-why-im-replacing-opus-in-claude - What we learned from 1,604 Chinese AI job postings
Inferring Chinese AI labs’ strategies from their job descriptions
- CPG and Retail Leaders Are Bullish on AI, yet Most Haven’t Scaled It Where It Matters Most
CPG and Retail Leaders Are Bullish on AI, yet Most Haven’t Scaled It Where It Matters Most Boston Consulting Group
- AI-powered software development: How technology is rewriting the rules
From individual productivity hacks to full workflow and role reinvention—three McKinsey experts explain how AI is changing the creation of software and what separates the companies capturing value.
- Better pay, clearer guidance: Investing in the working conditions of artificial intelligence data workers - ORA
Better pay, clearer guidance: Investing in the working conditions of artificial intelligence data workers ORA - Oxford University Research Archive
Score: 60🌐 MovesJun 24, 2026https://ora.ox.ac.uk/objects/uuid:0aaadfab-83d0-4e9b-a627-addec67f025b/files/r02870z54n - Scaling cybercrime disruption through innovation and AI
The post Scaling cybercrime disruption through innovation and AI appeared first on Source .
Score: 60🌐 MovesJun 24, 2026https://blogs.microsoft.com/on-the-issues/2026/06/24/scaling-cybercrime-disruption-through-innovation-and-ai/ - Qingyang's Computing-Power Synergy: How Green Electricity Powers China's New AI Data Center Hub
Qingyang's Computing-Power Synergy: How Green Electricity Powers China's New AI Data Center Hub
- Airlines turn to AI to save fuel; IndiGo to start trials for thriftier take-offs from today
Airlines turn to AI to save fuel; IndiGo to start trials for thriftier take-offs from today
- Why CIOs need to rethink enterprise content architecture for the LLM era
By Siddhartha Vanvani, Founder & CEO of DareAISearch Every CIO understands the cost of technical debt. Over the last two decades, enterprises have invested heavily in modernizing infrastructure, consolidating applications, […] The post Why CIOs need to rethink enterprise content architecture for the LLM era appeared first on Express Computer .
- Lowdown: SEBI wants to treat finfluencers and AI avatars as celebrities under the new ad code
SEBI's draft Common Advertisement Code unifies rules for seven intermediary types, drops prior approval for most ads, bans dark patterns, and classifies finfluencers and virtual avatars as celebrities. The post Lowdown: SEBI wants to treat finfluencers and AI avatars as celebrities under the new ad code appeared first on MEDIANAMA .
Score: 60🌐 MovesJun 24, 2026https://www.medianama.com/2026/06/223-lowdown-sebi-treat-finfluencers-ai-avatars-celebrities-under-new-ad-code/ - India can build sovereign AI, but can it build an AI-ready workforce?
India can build sovereign AI, but can it build an AI-ready workforce? YourStory.com
Score: 60🌐 MovesJun 24, 2026https://yourstory.com/2026/06/india-can-build-sovereign-ai-but-can-it-build-an-ai-ready-workforce - If an AI chatbot misleads you, who is to blame? | Bruce Schneier and Nathan E Sanders
A court in Germany found that Google was responsible for what its chatbots say in search summaries. This is the accountability we need Earlier this month, a German court ruled that Google is liable for its AI search summaries. Rejecting defenses like “users can check for themselves”, and that they generally know “that information generated with AI should not be blindly trusted”, the court held that the AI’s summaries are reflections of the company and “above all an expression of Google’s business activities”. This is the latest skirmish in a decades-old battle over internet publishing. Historically, there were two different types of information distributors: carriers and publishers. A phone company is a carrier. It’ll transmit whatever you say, even discussions about committing a crime. Words are words, and the phone company does not know – nor is it liable for – the words you choose to speak. A newspaper, on the other hand, is a publisher. It decides the words it publishes, and what quotes to include in its articles. If those words or quotes are defamatory or otherwise illegal, it’s liable. Continue reading...
Score: 60🌐 MovesJun 24, 2026https://www.theguardian.com/commentisfree/2026/jun/24/ai-errors-companies-responsibility - Korea's AI treasure is its factory data: Siemens top executive
South Korea is uniquely positioned in shaping the future of artificial intelligence, owing to its robust manufacturing sectors and the vast amounts of data generated by its factories, according to a top executive at Siemens. “The treasure we are sitting on as industrial countries is industrial data,” Cedrik Neike, member of the managing board at Siemens and CEO of Digital Industries, said in an interview with The Korea Herald on Tuesday in Seoul, referring to manufacturing powerhouses such as Ko
- Met gets extension to Palantir AI project after Sadiq Khan blocked deal
Mayor’s office grants extra 12 months to run pilot while London force procures long-term supplier The Metropolitan police have been granted a 12-month extension to a pilot project with the spy-tech firm Palantir while the force carries out a procurement process. The development comes weeks after the mayor of London, Sadiq Khan, blocked a £50m deal between the Met and the US company to automate intelligence analysis in criminal investigations. Continue reading...
- OpenAI Hires AWS Partnerships Chief in Business AI Push
OpenAI Hires AWS Partnerships Chief in Business AI Push The Information
Score: 60🌐 MovesJun 24, 2026https://www.theinformation.com/briefings/openai-hires-aws-partnerships-chief-business-ai-push - Google delays Gemini 3.5 Pro launch to July as it tweaks its new frontier AI model
Google delays Gemini 3.5 Pro launch to July as it tweaks its new frontier AI model Business Insider
Score: 60🤖 ModelsJun 24, 2026https://www.businessinsider.com/google-3-5-pro-july-release-tokens-ai-agents-model-2026-6 - How Home Depot is rebuilding retailing with AI
How Home Depot is rebuilding retailing with AI Fortune
- The Trump White House Is Over Anthropic CEO Dario Amodei
At high-stakes meetings with the White House, Anthropic's cofounder—a "weirdo," per one official—has been replaced by cofounder Tom Brown.
Score: 60🌐 MovesJun 24, 2026https://www.wired.com/story/the-trump-white-house-is-over-anthropics-dario-amodei/ - Setting achievable sustainability targets in the age of AI infrastructure
Artificial intelligence has fundamentally altered the sustainability conversation within enterprise IT. For years, organisations made steady progress in improving the efficiency of their digital estates – consolidating workloads, migrating to cloud platforms and embedding sustainability into procurement and reporting frameworks. Those efforts, while meaningful, were largely built around a predictable model of demand. AI changes that model entirely. High-density compute is no longer optional. It is becoming a core requirement for competitiveness, innovation and in some cases, operational survival. The challenge for CIOs is not whether to embrace it, but how to do so without undermining the sustainability commitments many organisations have spent years establishing. The reality is that traditional approaches to sustainability target setting are no longer sufficient. Targets must now be achievable, measurable and, critically, grounded in operational reality. Otherwise, there is a risk they become detached from the infrastructure strategies required to deliver business value. Moving from ambition to operationally-achievable targets One of the most common pitfalls in sustainability strategy is setting targets that look credible on paper but are disconnected from how technology is actually deployed and consumed. In an AI-driven environment , this gap becomes more pronounced. CIOs need to move away from broad top-down commitments and instead define targets that are embedded within infrastructure decision-making. That means aligning sustainability metrics directly to workload design, data management and hardware lifecycle planning. For example: Defining acceptable energy intensity thresholds for AI workloads, rather than treating all compute equally Establishing clear policies on model training frequency and dataset retention Embedding lifecycle extension targets for physical infrastructure alongside performance objectives. These are not headline-grabbing commitments, but are achievable, enforceable and capable of being audited. Sustainability, in this context, becomes less about aspiration and more about engineering discipline. Beyond market-based reporting A second challenge lies in how sustainability performance is measured and reported. Many organisations continue to rely heavily on market-based carbon accounting, supported by renewable energy certificates and offset mechanisms. While these have a role to play, they can create a misleading picture of actual environmental impact. The shift towards location-based reporting is therefore essential. Understanding where workloads run, how energy is generated in those locations and how grid intensity fluctuates over time provides a far more accurate reflection of environmental impact. It also enables more informed decision-making at an architectural level. However, this requires greater transparency than many organisations currently have access to. As highlighted in earlier discussions around cloud sustainability, provider-level reporting often lacks the granularity required for meaningful enterprise analysis. Without consistent methodologies and comparable data, CIOs are left working with approximations rather than auditable metrics. To address this, organisations need to combine external data with internal governance: Correlating workload placement with regional carbon intensity data Building internal reporting frameworks that standardise measurement across environments Challenging suppliers to provide more granular, verifiable data. Only then can sustainability targets move from indicative to defensible. Rethinking the AI refresh cycle Perhaps the most significant, and least discussed, sustainability risk associated with AI is the potential for accelerated hardware refresh cycles. The performance demands of AI workloads are driving rapid adoption of specialised infrastructure, particularly GPU-intensive environments. While this delivers clear capability gains, it also creates a temptation to prematurely retire existing assets in favour of new, optimised platforms. This is where sustainability strategy must take a more balanced view. The embodied carbon associated with manufacturing new hardware is substantial. In many cases, the environmental cost of early replacement outweighs the operational efficiency gains delivered by newer equipment. Extending the life of legacy infrastructure, where appropriate, therefore becomes a critical lever. This does not mean resisting innovation or compromising performance. It means adopting a more nuanced approach: Segregating workloads so that high-density AI compute runs on optimised platforms, while less intensive tasks remain on existing infrastructure Identifying opportunities for redeployment rather than wholesale replacement Integrating lifecycle extension and transition planning into procurement and refresh strategies. Crucially, organisations also need to consider what happens at the point of transition. Decisions made at end-of-life – whether assets are redeployed, reused, or prematurely retired – have a direct and often underappreciated impact on overall sustainability performance. In many cases, these moments represent one of the few points in the infrastructure lifecycle where outcomes can be fully measured, verified and audited, rather than inferred. Ignoring this stage risks undermining otherwise well-intentioned sustainability strategies. Sustainability as a differentiator While much of the sustainability conversation is framed in terms of risk mitigation or compliance, there is a growing opportunity for organisations to use it as a genuine differentiator. This is particularly true in sectors where clients, regulators and investors are placing increasing emphasis on verifiable environmental performance. The key word here is verifiable. Organisations that can demonstrate the following will be in a far stronger position than those relying on high-level claims or offset-driven narratives: Clear alignment between infrastructure strategy and sustainability targets Transparent, auditable reporting methodologies Responsible management of technology across its full lifecycle, including how assets are transitioned, redeployed and retired. In practice, this often comes down to control. Enterprises may have limited visibility into upstream infrastructure operated by hyperscale providers, but they retain direct control over how their own technology estate is managed, particularly at points of refresh, redeployment and end-of-life. Those control points provide a tangible foundation for building sustainability strategies that are not only credible, but defensible under scrutiny. In an environment where AI adoption is accelerating, this level of accountability becomes a meaningful differentiator. A shift in accountability Ultimately, the move towards sustainable AI infrastructure requires a shift in how responsibility is understood. It is no longer sufficient to view sustainability as a function of the datacentre operator or cloud provider alone. Enterprises themselves are active participants in driving demand and shaping outcomes. As discussed in the context of AI infrastructure more broadly, environmental impact is the cumulative result of countless individual decisions, from workload design to data retention to hardware refresh cycles. Importantly, some of the most impactful of these decisions occur at transition points within the lifecycle. How long assets are retained, how effectively they are redeployed, and how they are ultimately retired are not peripheral considerations. They are central to whether sustainability targets can be realistically achieved and evidenced. These are also areas where organisations have the greatest degree of control. CIOs therefore have a critical role to play. Not in limiting innovation, but in ensuring that innovation is delivered with a full understanding of its implications. Not just in production, but across the entire lifecycle of the technology that enables it. Conclusion The tension between AI adoption and sustainability is real, but it is not insurmountable. By focusing on achievable, operationally-grounded targets, moving towards more accurate and transparent reporting, and taking a lifecycle view of infrastructure, organisations can navigate this challenge effectively. In doing so, they not only protect their sustainability commitments, but create an opportunity to differentiate. Because in an AI-driven world, it will not be enough to demonstrate what your infrastructure can do. Increasingly, organisations will also be judged on how responsibly they choose to run it. For organisations looking to strengthen this aspect of their strategy, aligning infrastructure decisions with robust secure IT asset disposal practices can provide a practical foundation for achieving auditable sustainability outcomes. Read more about IT and sustainability How to improve AI efficiency beyond cost optimisation . With half of generative AI projects expected to overrun budgets by 2028, IT leaders must drive efficiency across the AI stack to protect margins and address environmental challenges What you need to know before emissions regulators come knocking . Carbon emissions reporting is becoming mandatory. But accounting is not the same as reducing, especially given the smoke and mirrors in some carbon footprint reporting
Score: 60🌐 MovesJun 24, 2026https://www.computerweekly.com/opinion/Setting-achievable-sustainability-targets-in-the-age-of-AI-infrastructure - AI was supposed to kill engineering jobs, but new data suggests they’re the most resilient
While AI dominates the layoff narrative, engineers are actually making up a larger share of new hires, according to SignalFire data.
- Alibaba Cloud Adds Fifth Japan Data Center as AI Platform Push Expands
Alibaba Cloud’s fifth Japan data center expands its Tokyo region and brings Model Studio to local customers, but enterprise buyers still need to verify residency, compliance, and roadmap details. The post Alibaba Cloud Adds Fifth Japan Data Center as AI Platform Push Expands appeared first on TechRepublic .
- Mistral launches OCR 4, turning document extraction into a full enterprise AI play
Mistral AI on Tuesday released OCR 4 , a document intelligence model that moves beyond raw text extraction to return structured representations of entire documents — complete with bounding boxes, block-type classification, and per-word confidence scores. The release marks Mistral's fourth generation of optical character recognition technology in roughly 15 months and lands at a moment when the company's pitch for European AI sovereignty has never been more commercially relevant. The model supports 170 languages across 10 language groups, accepts PDF, DOC, PPT, and OpenDocument formats, and can be deployed as a single container on an organization's own infrastructure — a capability Mistral is positioning directly at enterprises in regulated industries that cannot route sensitive documents through U.S.-jurisdiction cloud APIs. "Mistral OCR 4 extracts and structures content from a wide range of documents," the company said in its announcement. "Where previous generations focused on converting a page into clean text and tables, OCR 4 returns a structured representation of the document." The model is available immediately through the Mistral API , Document AI in Mistral Studio , Amazon SageMaker , and Microsoft Foundry , with Snowflake Parse Document support coming soon. Pricing starts at $4 per 1,000 pages, dropping to $2 per 1,000 pages through a batch API discount. OCR 4 treats every document as a semantic map, not a wall of text The central engineering shift in OCR 4 is structural. Rather than outputting a flat stream of extracted text — the paradigm that has defined OCR for decades — the model returns a layered representation in which every block is localized with a bounding box, classified by type (title, table, equation, signature, and others), and scored for confidence at both the page and word level. Mistral says bounding boxes were its most-requested capability. The reason is straightforward: without location data, downstream systems cannot trace an extracted fact back to its source on a specific page. That traceability gap has been a persistent friction point for enterprises building retrieval-augmented generation (RAG) pipelines, compliance workflows, or any application where "where did this number come from?" is a question that needs an auditable answer. Block classification addresses a related problem. A paragraph tagged as a "title" can segment a document into hierarchical chunks for semantic search. A block tagged as a "table" can be routed to a structured-data pipeline rather than a text summarizer. A block tagged as a "signature" can trigger a redaction workflow in a compliance system. These are not novel ideas in isolation, but packaging them as first-class outputs of the OCR model itself — rather than requiring a separate layout-analysis stage — removes an integration layer that enterprise teams have historically had to build and maintain themselves. The confidence scores serve a dual purpose. At scale, they allow organizations to programmatically route low-confidence regions to human reviewers and auto-approve high-confidence extractions, building what the industry calls human-in-the-loop verification without requiring a person to review every page of every document. In production systems, OCR is rarely the end goal — it is the first step in a larger pipeline. Developers building RAG systems, agent workflows, or document automation often spend more time reconstructing layout and structure than on the downstream AI logic itself. OCR 4 aims to eliminate that reconstruction step, and if it delivers on that promise, the value accrues not just in OCR cost savings but in reduced engineering hours across the entire document pipeline. Independent reviewers preferred Mistral's output 72 percent of the time, but benchmarks tell a complicated story Mistral reports that OCR 4 achieved a 72% average win rate in a head-to-head human evaluation against leading competitors, conducted by independent annotators across more than 600 real-world documents in over 12 languages. The model also achieved the top overall score on OlmOCRBench at 85.20 and scored 93.07 on OmniDocBench . But the company itself urges caution in interpreting those numbers. In its release, Mistral took the unusual step of auditing and publicly disclosing the specific types of scoring artifacts it encountered, including ground-truth errors in the reference annotations, equivalent LaTeX notation scored as mismatches, column-reading-order assumptions, and header/footer attribution issues. "We therefore treat the aggregate score as directional rather than definitive," the company said — a notably transparent stance from a vendor announcing a product. That transparency is well-timed. On the public OlmOCRBench leaderboard , some researchers have noted that OCR 4 currently ranks third, behind open models like Chandra OCR 2. And some open-weight models self-report higher OmniDocBench composite scores — PaddleOCR-VL-1.6 claims 96.33 — though those results have not been independently reproduced on the public leaderboard. Early enterprise feedback has been favorable nonetheless. Aidan Donohue, an AI engineer at financial AI firm Rogo, said the company benchmarked OCR 4 against leading agentic document parsers on a chart-dense financial QA dataset and "reached equivalent accuracy at roughly 8x lower cost and 17x lower latency." Ivan Mihailov, an AI engineer at intellectual property management firm Anaqua, said OCR 4 is "roughly 4x faster per page than our incumbent provider." Enterprise buyers, however, should run their own evaluations rather than relying on any vendor's benchmark numbers. The practical question is not which model scores highest on a leaderboard, but which model produces the fewest errors on your specific documents, in your specific languages, at a price and latency that fit your workflow. The Anthropic export ban gave Mistral's sovereignty pitch the proof point it needed Mistral's release lands in a geopolitical context that could hardly be more favorable for its strategic positioning. On June 12, Anthropic was forced to disable all access to its newest AI models , Fable 5 and Mythos 5, after the U.S. Commerce Department used national security export controls to bar the company from distributing the models to any foreign national. Enterprise clients in finance, healthcare, SaaS, and critical infrastructure found their core intelligence services abruptly disabled, without prior warning or effective recourse. As of June 24, both models remain offline, with prediction markets giving only 57% odds of restoration before July 1. That episode validated a warning Mistral CEO Arthur Mensch has been sounding for over a year. As Business Insider reported, Mensch warned at London Tech Week in June 2025 about American AI companies "having the keys" for their models, calling it a scenario where European companies are "giving leverage to their providers." He added: "At some point, you need to be able to turn it off or turn it on, and you don't want to leave it to another country." The argument gained further urgency as Mensch's broader sovereignty pitch escalated in recent months. As reported by CNBC in late May, Mensch told the outlet : "Europe is lagging behind when it comes to [the] buildout of infrastructure, and so we are investing to close that gap." At the same time, Mensch pushed back against Pope Leo XIV's call for AI to be "disarmed," arguing that Europe cannot afford to fall behind U.S. tech giants. "We're all for peace, but if you look at our rivals and adversaries in the world, they're using artificial intelligence … we do need to have our own capabilities," Mensch told reporters. OCR 4's single-container, self-hosted deployment model is the product-level expression of that argument. A U.S.-headquartered provider offering EU data residency means documents are stored in Frankfurt but governed by U.S. law. Mistral, incorporated in France and operating under EU jurisdiction, offering on-premise containerized deployment, means documents never leave the customer's infrastructure at all. The EU AI Act's fine enforcement provisions take effect August 2, adding regulatory pressure to the compliance calculus for European enterprises evaluating document AI vendors. Baidu's free, open-weight OCR model arrived one day earlier — and the contrast is revealing Mistral's release did not arrive in isolation. Just one day before OCR 4 launched, Baidu shipped Unlimited-OCR on June 22 — a 3-billion-parameter MIT-licensed model that tackles one of the most persistent pain points in document AI: parsing entire PDFs and multi-page scans in a single forward pass, without chunking the input or stitching the output back together afterward. Baidu's model uses a technique called Reference Sliding Window Attention (R-SWA) that, as a top Hacker News commenter explained , splits the AI's focus into two paths: maintaining full attention on the original document image while restricting memory of generated text to a tight, moving window. The result is constant KV cache size and the ability to transcribe 40-plus pages in a single forward pass. The model gathered 1,800 GitHub stars in its first 24 hours and racked up more than 479 upvotes on Hacker News , where the discussion thread ran to 109 comments. The two releases frame what some analysts are calling the June 2026 document-AI split: self-hosted long-horizon parsing with open weights versus structured managed extraction with enterprise features. Baidu's model is free under an MIT license, runs on standard GPU hardware, and has no managed API or enterprise SLA. Mistral's model is a commercial product with per-page pricing, bounding boxes, confidence scores, block classification, multi-platform distribution, and self-hosted deployment options for enterprise customers. Unlimited-OCR may be the better tool for a research team digitizing scanned dissertations on a single GPU. OCR 4 is built for the IT procurement process — the world of SLAs, data processing agreements, and compliance audits. Beyond Baidu, the broader OCR competitive field includes Google Document AI , Amazon Textract , Azure Document Intelligence , ABBYY Vantage , and a growing number of open-weight models. On the Hacker News thread for Unlimited-OCR, practitioners offered a candid assessment of the state of the art. Joss82, who has worked on document parsing for 10 years, wrote bluntly: "OCR still sucks in 2026." Meanwhile, one user named SyneRyder reported success with Claude for OCR of hundreds of pages of handwritten documents, noting the model delivered results with "no corrections required" and even pointed out a continuity error in the source text. These practitioner reports underscore a key tension in the market: performance varies wildly depending on the specific document type, language, and quality of the source material. The real play is not OCR — it is an enterprise AI stack with document intelligence as the on-ramp Step back far enough, and Mistral's OCR 4 release is not really an OCR story. It is an enterprise go-to-market story built on top of a $4.4 billion global intelligent document processing market that is forecast to grow at a 33.1% compound annual growth rate through 2030, according to Grand View Research . For Mistral, OCR is a wedge into enterprise AI budgets. The model feeds directly into Mistral's Search Toolkit , the company's open-source composable search framework announced at the AI Now Summit. In that architecture, OCR 4 serves as the ingestion layer for retrieval-augmented generation and enterprise search pipelines, converting raw documents into citation-ready, structurally classified input. The logic is clear: once an enterprise adopts OCR 4 for document extraction, Mistral's broader model suite — including Medium 3.5 for reasoning and the Vibe agentic platform for task execution — becomes the natural next step in the stack. That pipeline ambition is critical context for understanding Mistral's current fundraising trajectory. Bloomberg recently reported that the company is in early discussions to raise about €3 billion ($3.5 billion) at a valuation of roughly €20 billion — nearly double the €11.7 billion valuation from its September Series C round. To date, Mistral has raised only about $4 billion, a fraction of what its largest U.S. rivals have taken in. OCR 4 and its associated enterprise revenue pipeline are part of how the company plans to justify that higher valuation, with Mistral targeting €1 billion in revenue for 2026, up from €200 million in 2025, according to Le Monde. Mistral is a company with roughly 1,000 employees and ambitions to compete with labs that have raised 40 times as much capital. It cannot win a general-purpose model arms race against OpenAI and Anthropic. What it can do is build a differentiated enterprise stack around sovereignty, structured document intelligence , and agentic workflows — and use that stack to capture European enterprise budgets that are increasingly wary of U.S. provider dependency. The pricing structure reinforces that strategy: at $2 per 1,000 pages in batch mode, the cost of processing a 100,000-page corporate archive falls to $200, making large-scale digitization projects economically viable in ways they may not have been with token-based vision-language model pricing. Whether Mistral can execute that vision at scale — against Google, Amazon, Microsoft, and a surging open-source ecosystem — remains an open question. But the Anthropic export control crisis is still unresolved, European data sovereignty regulations are tightening, and a potential €20 billion funding round is on the horizon. The company is holding an OCR 4 production webinar on July 7 at 6:00 PM CET . Two weeks ago, the argument for building AI infrastructure outside the reach of U.S. export controls was theoretical. Then the U.S. government flipped a switch, and Anthropic's most advanced models went dark for every non-American on the planet. Mistral did not cause that crisis — but it spent the last year building the product that makes it matter.
Score: 59🤖 ModelsJun 24, 2026https://venturebeat.com/data/mistral-launches-ocr-4-turning-document-extraction-into-a-full-enterprise-ai-play - Pentesting can’t keep up with AI coding, report
Pentesting can’t keep up with AI coding, report Computing UK
Score: 59🌐 MovesJun 24, 2026https://www.computing.co.uk/news/2026/security/pentesting-can-t-keep-up-with-ai-coding-report - Tencent QQ Mail Launches Agently Mail, Enabling Autonomous Email and Business Coordination for AI Agents
Tencent has launched Agently Mail, a dedicated email service purpose-built for AI agents, now in beta testing. The product directly addresses two core pain poin...
- NTT DATA and Cursor Partner to Accelerate Enterprise-Grade Modernization and AI Governance
NTT DATA, a global leader in AI, digital business and technology services, today announced a strategic partnership with Cursor, the leading multi-model AI coding platform.