AI News Archive: July 10, 2026 — Part 6

Sourced from 500+ daily AI sources, scored by relevance.

• “1~2시간 걸리던 장애 분석, 5분이면 끝”…데이터독 ‘비츠 AI’ 전면에

  데이터독 코리아는 9일 서울에서 기자간담회를 열고 이 같은 사업 현황과 연례 컨퍼런스 ‘ 대시 (Dash) 2026’에서 공개한 신제품 전략을 소개했다. 엄수창 데이터독 코리아 지사장은 이를 시장 변화의 신호로 해석했다. 엄 지사장은 “연초만 해도 ‘SaaS 아포칼립스’라는 말이 나올 정도로 SaaS 기업들의 미래를 부정적으로 보는 시각이 많았다”며 “하지만 데이터독은 AI와 함께 성장하면서 오히려 큰 미래 비전을 갖게 됐다”고 말했다. 이어 “글로벌 상위 AI 기업 10곳 모두 데이터독을 사용하고 있다는 사실 자체가 시장이 우리의 성장 가능성을 높게 평가하고 있다는 방증”이라고 강조했다. 이 같은 자신감의 배경에는 AI 시대 급증하는 운영 관리 수요가 있다. 과거에는 데브옵스(DevOps)와 SRE(Site Reliability Engineering) 조직이 장애 분석과 인프라 모니터링을 위해 주로 활용했다면, 이제는 AI를 활용해 운영을 자동화하는 기능과 AI 애플리케이션 자체를 관리하는 기능까지 제공하며 AI 시대에 맞춰 사업 영역을 확대하고 있다. 정영석 데이터독 기술총괄은 올해 대시에서 공개한 100여 개의 신기능을 ‘자율 IT 운영(Autonomous Operations)’과 ‘AI 거버넌스’라는 두 가지 축으로 설명했다. 자율 IT 운영 분야에서는 ‘비츠 AI(Bits AI)’가 장애 탐지부터 원인 분석, 해결까지 자동으로 수행한다. 정 총괄은 “장애가 발생하면 비츠 AI가 8가지 안팎의 가설을 세운 뒤 하나씩 검증해 근본 원인을 찾아내고, 코드 수정안까지 PR(Pull Request) 형태로 제안한다”며 “기존에는 엔지니어가 1~2시간 걸리던 분석 및 보고서 작성 작업을 빠르면 5분 이내로 단축할 수 있다”고 설명했다. 또 인프라 자원이 부족하면 슬랙 등을 통해 운영자 승인만 받아 메모리와 CPU를 자동으로 증설하고, 사전에 정의한 가드레일 안에서는 무인 복구도 수행한다. 코드 변경부터 스테이징 배포, 프로덕션 환경에 이르기까지 애플리케이션이 의도대로 동작하는지도 AI가 지속적으로 검증한다. AI 거버넌스 분야에서는 ▲에이전트 옵저버빌리티(Agent Observability) ▲AI 게이트웨이(AI Gateway) ▲AI 가드(AI Guard) ▲LLM 비용 관리 콘솔 등의 기능 공개했다. 에이전트 옵저버빌리티는 AI 에이전트 내부에서 어떤 LLM과 도구를 사용했고, 토큰과 비용이 얼마나 발생했는지 시각화한다. AI 게이트웨이는 여러 LLM을 통합 관리하고 감사(Audit)를 수행하며, AI 가드는 프롬프트 인젝션과 민감정보 유출을 차단한다. 정 총괄은 “코파일럿, 커서(Cursor), 클로드 등 여러 AI 모델을 함께 사용하는 기업이 늘면서 비용과 보안, 신뢰성을 통제하는 것이 C레벨 경영진의 공통 과제가 됐다”며 “개발자별, 모델별 사용량과 비용을 세분화해 보여주기 때문에 임원들도 최적화 지점을 쉽게 찾을 수 있다”고 말했다. 최근 많은 기업이 멀티 LLM 전략을 채택하는 만큼 이러한 통합 관리 플랫폼의 필요성도 커질 것이라는 설명이다. CIO 코리아가 AI 기능 추가로 관련 비용이 늘어나는 것 아니냐고 묻자 정 총괄 “데이터독이 제공하는 수백 개의 외부 서비스 연동 기능은 모두 기본 호스트 사용료에 포함돼 있어 AI 기능이 추가됐다고 모니터링 비용이 늘어나는 것은 아니다”며 “다만 로그는 저장량이 늘어나면 비용이 증가하는 구조인데 이는 어느 벤더나 비슷하다”고 답했다. 이어 “AI가 추가됐다고 인프라 모니터링 비용이 올라가는 것은 아니지만 AI SRE처럼 자동 분석 기능을 사용할 경우 토큰(크레딧)이 소모돼 비용이 추가될 수 있다”며 “반면 엔지니어의 업무 시간을 크게 줄일 수 있기 때문에 ROI 측면에서는 충분히 상쇄할 수 있고, UI 대신 MCP(Model Context Protocol)를 활용하면 비용을 낮출 수 있어 국내 여러 대형 고객도 MCP 기반 AI옵스를 구축하고 있다”고 덧붙였다. AI가 문제 해결책까지 제시하는 것에 대한 고객사의 거부감은 없는지 묻는 질문에는 “잘못 분석할 가능성이 있는 것은 사실이지만 지금까지 고객 반응은 매우 긍정적”이라며 “AI가 잘못 탐지하더라도 사용자가 대화를 통해 추가 분석을 요청하면 계속 수정하면서 근본 원인에 더 가까운 결과를 제시한다”고 설명했다. 이어 “이 경험은 ‘비츠 메모리(Bits Memory)’ 기능에 축적돼 이후 유사한 장애가 발생하면 더욱 정확하게 분석하도록 학습된다”고 말했다. 내부 AI는 자체 모델과 업계 최신 모델을 함께 사용하는 하이브리드 구조다. 정 총괄은 “프론티어 모델과 자체 모델인 ‘ 토토 (Toto)’를 함께 운영하고 있으며 작업 특성에 따라 가장 적합한 모델을 선택해 사용한다”고 밝혔다. 클로드, GPT, 제미나이 등 다양한 외부 최신 모델을 활용하며, AI SRE는 내부적으로 최적 모델이 자동 선택되지만 에이전트 빌더에서는 고객이 MCP와 사용할 모델을 직접 선택할 수 있다. 또한 LLM 옵저버빌리티에서는 환각을 탐지하기 위해 교차 검증용 모델을 별도로 지정하는 기능도 제공한다. 데이터독은 앞으로 옵저버빌리티(Observability), 보안, 핀옵스(FinOps), 비즈니스 인텔리전스(BI)를 하나의 플랫폼에서 통합 제공하는 차별성을 앞세워 국내 시장 공략을 확대하겠다고 밝혔다. jihyun.lee@foundryco.com

  Score: 52🌐 MovesJul 10, 2026https://www.cio.com/article/4195338/12%ec%8b%9c%ea%b0%84-%ea%b1%b8%eb%a6%ac%eb%8d%98-%ec%9e%a5%ec%95%a0-%eb%b6%84%ec%84%9d-5%eb%b6%84%ec%9d%b4%eb%a9%b4-%eb%81%9d%eb%8d%b0%ec%9d%b4%ed%84%b0%eb%8f%85-%eb%b9%84%ec%b8%a0-ai.html
• Business Brief (July 10): Chinese AI Stocks Surge After Goldman Sachs Report

  Business Brief (July 10): Chinese AI Stocks Surge After Goldman Sachs Report Caixin Global

  Score: 52🌐 MovesJul 10, 2026https://www.caixinglobal.com/2026-07-10/business-brief-july-10-chinese-ai-stocks-surge-after-goldman-sachs-report-102462784.html
• Debatable: AI’s impact on the economy

  Will the technology eliminate US jobs, or create more of them? Washington isn’t sure.

  Score: 52🌐 MovesJul 10, 2026https://www.semafor.com/article/07/09/2026/debatable-ais-impact-on-the-economy
• Microsoft warns customers AI will mean busier Patch Tuesdays

  More patches mean more reasons to buy Redmond’s auto-patching tools

  Score: 52🌐 MovesJul 10, 2026https://www.theregister.com/security/2026/07/10/microsoft-warns-customers-ai-will-mean-busier-patch-tuesdays/5269618
• Sabarimala rolls out AI crowd control, digital reforms

  Sabarimala rolls out AI crowd control, digital reforms Gulf News

  Score: 52🌐 MovesJul 10, 2026https://gulfnews.com/world/asia/india/sabarimala-unveils-sweeping-reforms-before-pilgrimage-1.500603671
• ‘Christ almighty, this is so bad’: ChatGPT’s big app update brings huge changes to your workflows — and users seem to hate it

  OpenAI has merged the ChatGPT app with Codex, but many users feel disappointed with the change.

  Score: 50🌐 MovesJul 10, 2026https://www.techradar.com/ai-platforms-assistants/christ-almighty-this-is-so-bad-chatgpts-big-app-update-brings-huge-changes-to-your-workflows-and-users-seem-to-hate-it
• Canada's Gulf investment ties deepen with Humain AI collaboration

  Canada's Gulf investment ties deepen with Humain AI collaboration thenationalnews.com

  Score: 50🌐 MovesJul 10, 2026https://www.thenationalnews.com/future/technology/2026/07/09/cohere-humain-canada-saudi-arabia-ai/
• ChatGPT's New Voice Models Aim for More Human-Like Conversations

  ChatGPT's New Voice Models Aim for More Human-Like Conversations PCMag Middle East

  Score: 50🌐 MovesJul 10, 2026https://me.pcmag.com/en/ai/37644/chatgpts-new-voice-models-aim-for-more-human-like-conversations
• Meta axes feature that allowed tagging Instagram users to generate AI images of them

  Meta axes feature that allowed tagging Instagram users to generate AI images of them CBC

  Score: 50🌐 MovesJul 10, 2026https://www.cbc.ca/news/business/meta-instagram-change-ai-settings-9.7265448
• The fraud officer in Yogyakarta won’t catch the AI wave, and most ASEAN institutions don’t know it yet

  The fraud officer I sat with in Yogyakarta last month had spent eleven years catching counterfeit invoices, suspicious wire transfers, and informal collusion patterns at a regional bank’s branch network. She is exceptionally good at her job. She has also spent the last three weeks trying to learn how to detect a deepfake — and […] The post The fraud officer in Yogyakarta won’t catch the AI wave, and most ASEAN institutions don’t know it yet appeared first on e27 .

  Score: 50🌐 MovesJul 10, 2026https://e27.co/the-fraud-officer-in-yogyakarta-wont-catch-the-ai-wave-and-most-asean-institutions-dont-know-it-yet-20260709/
• Shiprocket launches AI-powered checkout Fastrr

  Shiprocket has launched Fastrr, an AI-powered checkout and pre-order suite that unifies fragmented commerce tools into a single platform, enabling brands to personalise experiences, reduce friction and improve conversion rates.

  Score: 50🌐 MovesJul 10, 2026https://cio.economictimes.indiatimes.com/news/artificial-intelligence/shiprocket-launches-ai-powered-checkout-fastrr/132306606
• AI Safety Policy Needs to train Legal Practitioners

  I completed my law degree at a working-class London university. In my first year, I was 18 years old, and I was often the youngest person in the room: almost everyone else was a paralegal, clerk or caseworker with years of live files behind them, studying part-time to qualify for the job they pretty much already did. But all four years, he same scene played out over and over: A mature student would answer from experience, and the teacher would say: “No. This is not right.” The mature student would go: “But this is exactly how I dealt with my case yesterday.” The teacher would eventually settle it with something along the lines of “At the end of the day, this is what you need to pass your exam.” The more it happened, the more I understood why people would tell me “nothing in practice happens how they teach it in school”. One term, a practising barrister covered for a teacher. He was in court every morning, and teaching in the afternoons. The first thing he did was telling us to get the practitioner’s handbook he used instead, and taught is using examples from his real cases. When the regular teacher returned, they were horrified. The barrister was reprimanded with a “none of that will be in the exam, and the students will be marked down if they don’t answer per the curriculum”. Nobody said the barrister was wrong about anything he taught us: Even if, eventually, our manuals would be replaced by practice notes (written by people like him), “theory was theory and practice was practice”. Little did I know, this was the first lesson about Policy I ever got. The profile the field is short of An over-simplification but fun visual of how I see this. The implementation side needs people who are academically literate enough to read the research as it actually is, and close enough to practice to know where the gaps are and how people will exploit them. This requires roaming around rooms: the room where policymakers sit, the room where legal & best practice standards get written, and the room where people who are “in charge of implementing the law” are- and are trained on how to do so. People with legal backgrounds who are technically literate enough to follow AI Safety conversations, are already scarce. I am lucky to have met a few of those rare hybrids. And, almost unavoidably, they end up in the first or the second room. That is not a bad thing: it just means not enough people are at the other side. The side that’s currently “someone else’s problem” in the field. The side with the people in charge of hearing cases about AI psychosis unaliving someone, approving mass deployments of AI Agents under the guise of “low risk” without understanding the technical implications, wearing the “AI Governance hat” in the market. The GDPR as the obvious example In case you’re not familiar with the General Data Protection Regulation , it is that European law that almost every “Privacy Policy” quotes, regardless of where you are in the world. Its reach and impact (in terms of having influenced business operations across the globe, not only in Europe) is one of the typical examples you’ll hear about “ the Brussels effect ”. Something interesting about this law is that it mandated the existence of the very function responsible for implementing it. Well, how do you make sure that companies follow the law? With enforcement actions, and by making Guidelines available to those who bother to read them. Luckily for us, with the GDPR, we thought about the problem of “what happens once I finished drafting the perfect law”. We decided to create the “Data Protection Officer”: an individual with enough “professional qualities” to be able to make companies comply. Articles 37 to 39 mandated this role to report to the highest level of management, to remain totally independent and without conflicts that would stop them from excercising their judgment. On paper, this looks like a massive policy victory, right? We mandated the existence of the gatekeeper. And, while arguably it really was a great feat, it does not seem as straight-forward when we look at what practice returned. In 2023, the European Data Protection Body (EDPB) ran a coordinated enforcement action across 25 supervisory authorities, analysing more than 17,000 responses on the position of DPOs. Sadly, the findings read like a checklist of everything Article 38 was supposed to prevent: insufficient resources, insufficient expert knowledge, DPOs not entrusted with the tasks the law assigns them, conflicts of interest, lack of independence, no reporting line to top management. Noyb’s survey of more than 1,000 data protection professionals found that 46% of appointed DPOs reported active pressure from sales and marketing to limit compliance, 32% report pressure from senior management, and 74% say that authorities would find relevant violations if they walked through the door of an average company. And the enforcement side that was supposed to back these practitioners up? Not so great, either. Noyb’s five-year review of its own 800+ complaints found that 85.9% were undecided, with more than 58% waiting over eighteen months for an answer. Per FOI data released in January 2026, over six years the DPC levied roughly €4.04 billion in fines, of which €4.02 billion remained uncollected and only about €20 million had been paid … How was this possible, when the policy-makers even thought about putting someone in the room to prevent this from happening? “OK, so the EU failed at embedding a gatekeeper for its privacy law. What does this have to do with AI Risk?” What if I told you that these same people are pretty much in charge of AI Risk in corporations? The IAPP’s Privacy and AI Governance Report shows AI governance is being built directly on top of privacy infrastructure: more than 50% of respondents designing AI governance approaches are building on top of privacy programs, and more than 40% are using existing privacy assessments to manage AI risk. Page 11 of the " AI Governance in Practice Report 2025 ". Note the depressing "49%" in "Lack of understanding of AI and underlying technologies". This is not only the case in the EU: The AI Governance in Practice Report 2025 , drawing on North American and European firms alike (Mastercard, TELUS, BCG, Kroll, IBM, Randstad, Cohere), found that when it broke down where AI governance sits organizationally, privacy and legal each hold 22% of the seats, IT 17%, data 10%, ethics 6%, and security 5%, and crucially that privacy ownership yields 67% EU AI Act confidence versus IT's 36%, and ethics 74%. And another thing: the “ AI Governance Practitioner Certification ” that the International Association of Privacy Professionals offers, is one of the best-known “AI Governance Certifications”. This is the path that a large majority of people in Data Protection go through when they realise they have to “upskil in AI”. As someone who’s been there: personally, I am scared of having the majority of the people on the other side, the legal implementation side, the “what happens after we pass the perfect law and companies just “have to comply” side… pretty much unaware of AI Safety fundamentals. But if pretty much anyone engaging with AI Safety, with a legal background, gets pushed towards the first pillar (Traditional Policy and Policy Research), who’ll be left to hold the fort and train the other? As critical as I am of my own, I need to say that GCs, Privacy & Compliance, DPOs and the current “AI Governance” frontline in corporate, tend to bring a lot of valuable experience and fresh data to the table. If your job is to anticipate non-compliance, you need people who have watched non-compliance being manufactured from the inside. And if you are the person who needs to help someone seek justice from AI-enabled harms, I’d hope you’re both experienced in legal action and aware of the technical concepts that influenced the behaviour that led to the harm. That’s why I also do not believe that the answer to this is just “recruit people from universities that are mission aligned to implement the law in companies”: Practice takes… practice. And, if we really anticipate timelines to be short, I think training the people who are already fluent in implementation to understand AI Safety, is worthwhile. Taking the silo down I know that it’s important that mission-aligned people dedicate themselves to policy activism and policy for stronger AI regulation. I 100% support this. But we already have a lot of tech law that is poorly implemented- being used as the “starting point” for the implementation of new one. From practice (mine and that of others tremendously more experience), I believe that not training the people whose jobs will be to implement it on how to do so, is a big cause of that. Sometimes, it starts with inviting such people in! I know it’s challenging, but this field is young enough to choose differently, and there are some easy enough ways to start doing this. If you run a conference: invite legal practitioners, not only policy researchers. For example, I really appreciate IASEIA for attempting to get Industry and Research talking every year. If you do legal and policy research: if your focus is on filling legal gaps, consider finding a person who does your topic for a living, and ask them how they’d see it breaking down it practice. Part of my contribution to this was organising trainings on AI Safety basics for highly motivated, corporate AI Governance professionals on AI Safety. Backed by my employer, EquiStamp, I am now assisting ML4Good , with the first iteration of The European Seminar on Frontier AI and Law . The idea is to bring the people who are in practice, lawyers, DPOs, compliance officers, privacy teams, in-house counsel, product counsel, GCs, into contact with AI safety fundamentals, adapted to the concepts they already use so that the knowledge is consolidated. To readers who happen to know anyone that may be a good fit: Feel free to invite them. To organisations running programs (fellowships or trainings) specifically aimed at bringing more people into policy: help me make sure that both sides talk. Disclosure: I am Head of Legal at EquiStamp , an AI safety evaluations company. This post reflects my personal opinion only. Discuss

  Score: 50🌐 MovesJul 10, 2026https://www.lesswrong.com/posts/MaXtWhyArguty23Mi/ai-safety-policy-needs-to-train-legal-practitioners
• The Whirlwind 72 Hours of Rival AI Announcements

  The Whirlwind 72 Hours of Rival AI Announcements Business Insider

  Score: 50🌐 MovesJul 10, 2026https://www.businessinsider.com/new-ai-model-announcements-openai-meta-grok-2026-7
• Do Social Media Bans Work? + A Conversation About A.I. Consciousness + Tool Time

  “If the net result is that all the teens in Australia are still using social media, even after they’re technically banned from doing it, why are we doing any of this?”

  Score: 50🌐 MovesJul 10, 2026https://www.nytimes.com/2026/07/10/podcasts/hardfork-social-media-bans.html
• SpaceX's near-term AI payoff seen tethered to Earth, not outer space

  SpaceX's near-term AI payoff seen tethered to Earth, not outer space Reuters

  Score: 50🌐 MovesJul 10, 2026https://www.reuters.com/science/spacexs-near-term-ai-payoff-seen-tethered-earth-not-outer-space-2026-07-10/
• Meet LingBot-World-Infinity: An Open Causal World Model With An Agentic Harness

  Meet LingBot-World-Infinity: An Open Causal World Model With An Agentic Harness MarkTechPost

  Score: 50🤖 ModelsJul 10, 2026https://www.marktechpost.com/2026/07/09/meet-lingbot-world-infinity-an-open-causal-world-model-with-an-agentic-harness/
• Valley Bank exec: AI is changing build-versus-buy question

  The New Jersey-based lender is using tiered employee access to control AI costs while pursuing internal efficiencies and relationship-building use cases, its chief operating officer said.

  Score: 50🌐 MovesJul 10, 2026https://www.bankingdive.com/news/valley-bank-ai-strategy-roi-vendors-midsize-lenders/824927/
• Law Firm Disrupted: The 'Ask Jeeves' Phase of Gen AI Evolution?

  While most lawyers are using generative AI, many say inefficiencies reduce its effectiveness, for now.

  Score: 50🌐 MovesJul 10, 2026https://feeds.feedblitz.com/~/960017573/0/law/legal-news~Law-Firm-Disrupted-The-Ask-Jeeves-Phase-of-Gen-AI-Evolution/
• ChatGPT is coming for one of Google’s smartest Chrome features

  OpenAI's new ChatGPT Chrome extension brings context-aware AI directly into your browser, challenging Google's Gemini Side Panel with webpage summaries, explanations, and task automation.

  Score: 50🌐 MovesJul 10, 2026https://www.digitaltrends.com/computing/chatgpt-is-coming-for-one-of-googles-smartest-chrome-features/
• SolarWinds World Tour Nairobi: Driving partner growth and AI-powered observability in East Africa

  A major focus of the event was the introduction and deep dive into SolarWinds SW1, the company’s new agentic AI-powered observability solution.

  Score: 50🌐 MovesJul 10, 2026https://www.itweb.co.za/article/solarwinds-world-tour-nairobi-driving-partner-growth-and-ai-powered-observability-in-east-africa/PmxVE7KEGwDqQY85
• Teachers are worried about students cheating with AI, but my survey suggests the deeper issue is learning

  As schools consider AI guidelines, educators are also thinking about how they can adjust their assignments to accurately measure what students are actually learning.

  Score: 50🌐 MovesJul 10, 2026https://theconversation.com/teachers-are-worried-about-students-cheating-with-ai-but-my-survey-suggests-the-deeper-issue-is-learning-286425
• Data centers don’t pay their ‘fair share’ of electricity costs. Here’s why

  Many major tech companies have pledged to pay their fair share of the costs associated with generating and transmitting more electricity to serve large data centers . But ratepayers across the United States are worried about the potential costs they might have to bear. That’s because it’s not immediately clear how the cost of data centers’ energy will be calculated. The effects of price increases are likely just beginning, and their full effects may not be felt for years. For example, a recent report by the organization that monitors the PJM market , an area that encompasses all or part of 14 mid-Atlantic and Midwest states, concluded that expected power demand from data centers was a primary reason for $23 billion in customer price increases that will last until at least the end of 2028. I have studied the programs states have launched to address the needs of these large electricity customers . Prices are set by state utility commissions , who determine which customers’ rates will increase by how much to pay for new investments in electricity infrastructure. It’s not simple. The complexity of setting prices Setting a price for electricity is straightforward in principle but complicated in execution. Regulators identify the costs to provide service, allocate the costs to customers, and design prices to recover those costs. First, regulators identify the costs that a utility company incurs to provide service . Regulators look at the value of the assets the utility company invests in, such as power plants, transmission lines, and substations, as well as its day-to-day operating expenses, such as salaries, fuel, replacement parts, and electricity it purchases from other sources. Then these costs are allocated to categories of customers, such as residential, commercial, and industrial. Ideally, costs are allocated to the customers who cause them, but that can be complicated to determine. For example, imagine a data center is built in an area that lacks existing power lines and is located 50 yards from a nearby electric substation. It’s clear that the data center should pay to run a 50-yard power line from the substation to the data center. But what if the power company needs to upgrade the substation to handle the increased needs of the data center? Or secure additional sources of electricity? In these cases, the investments are part of the electricity grid that everyone uses. These costs will likely be shared among all customers. Cost analysts review each line of a utility company’s costs, often thousands of items, and determine how each cost will be allocated. Each decision incorporates one basic idea: What’s your share ? For instance, if a group of customers uses 20% of the electricity delivered by the utility, they would be allocated 20% of the costs associated with energy delivery. Other cost items may be allocated based on the number of customers or how much electricity customers use at particular points in time, but the idea is the same. Finally, the analysts set prices that are designed to recover the costs allocated to each customer group. So, the costs that are allocated to you are directly reflected in the electricity prices that you pay. Flexibility and a potential loophole One common criterion for figuring out how much a customer should pay is based on what is called “coincident peak demand”—the amount a customer group uses at the moment when all customers are collectively using the largest amount of electricity. Costs associated with overall peak usage are typically split proportionally—but this opens an opportunity for data centers to exploit the system. Data centers often are able to fine-tune their electricity consumption, using more one minute and less another , in ways that residential users can’t easily replicate. Computerized systems can automatically adjust the amount of work a data center is doing, while a homeowner would either have to race around shutting off appliances to meaningfully reduce the amount of power their home was using or invest in a device that does. Their flexibility means data centers may be able to learn to predict when system loads will peak and consume little to no power in just the right period to avoid contributing to peak loads, as has happened with cryptocurrency-mining operations in Texas. So when regulators look at their usage to determine prices, data centers may be able to avoid paying any costs allocated through coincident peak demand, even if they use large amounts of electricity at other times. Who speaks for you? When utility regulators decide how costs should be allocated to each customer group, they solicit input from different groups. The utility company initially submits its own proposal for how it thinks costs should be allocated across its system. Large industrial customer groups representing customers such as factories will also submit their own proposals for how to allocate costs and set rates. Retail customer groups representing large and small stores will submit theirs. And large data centers, with the resources to hire experts in cost allocation, will submit theirs as well. Some states have specific state-government agencies to do some of this work on behalf of particular commercial groups, such as Pennsylvania’s Office of Small Business Advocate . Regulators don’t always get a good sense of residential customers’ voices, though. Every state except Georgia, Idaho, and Louisiana has an office of the consumer advocate that represents customer interests in proceedings before the state utility regulator. But they are often charged with representing all customers in the state without bias, meaning they cannot advocate for outcomes that would impose costs on one group of customers in favor of another. So while every state’s consumer advocate is concerned with keeping the utility’s costs as low as possible, they may be barred by law from adopting a position on how those costs should be allocated. This lack of representation in this aspect of rate-setting for average households may lead to situations where the data centers’ advocates argue for minimal costs to be allocated to them—but nobody advocates on behalf of residents to examine or refute that argument. Citizens left holding the bag There are other risks for residential customers, too. Utilities’ investments in electricity infrastructure last for many years. But not every proposed data center will get built , and some may use less energy than originally projected . Technology may even change, making some data centers obsolete after a year or two of operations. If those events happen, then any costs the utility company incurred to provide enough electricity will be spread among all the other customers . The allocation process may be even more complicated for municipal utilities regulated by city councils or independent boards, or cooperative utilities regulated by elected boards in rural communities. These groups may not have full-time staff who are utility or regulatory experts, yet they face the same decision-making challenges as trained professionals and might have to retain outside experts to aid in the process. Consumers need to be aware of the importance of cost allocation and how it affects their electricity rates. I believe they should provide public comments to the regulators and speak during open hearings, as there may not be anyone else effectively advocating for their interests. Theodore J. Kury is a director of energy studies at the University of Florida . This article is republished from The Conversation under a Creative Commons license. Read the original article .

  Score: 49🌐 MovesJul 10, 2026https://www.fastcompany.com/91569757/data-centers-fair-share-electricity-costs?partner=rss&utm_source=rss&utm_medium=feed&utm_campaign=rss+fastcompany&utm_content=rss
• Special delivery: Italy's postman joins the AI infrastructure race

  Special delivery: Italy's postman joins the AI infrastructure race Reuters

  Score: 49🌐 MovesJul 10, 2026https://www.reuters.com/business/media-telecom/special-delivery-italys-postman-joins-ai-infrastructure-race-2026-07-10/
• Altera returns to growth as AI, robotics fuel demand, CEO says

  Altera returns to growth as AI, robotics fuel demand, CEO says Reuters

  Score: 49🌐 MovesJul 10, 2026https://www.reuters.com/business/altera-returns-growth-ai-robotics-fuel-demand-ceo-says-2026-07-10/
• Smart bots, big bills: India Inc’s rude awakening after AI fails to lower consulting fees

  Despite AI reducing junior-level workload, India's top consulting and audit firms say fees haven't dropped. AI risks like hallucinations demand more senior oversight, raising costs. Firms are shifting toward value-based pricing tied to outcomes.

  Score: 49🌐 MovesJul 10, 2026https://www.livemint.com/companies/news/india-inc-consulting-firms-advisory-firms-consulting-fees-client-billing-automated-work-senior-partners-ai-11783586546604.html
• Syracuse University to Offer New AI Degrees to Boost Enrollment

  Facing a $30 million budget deficit this year, Syracuse University will offer new bachelor’s and master’s degrees focused on teaching students how best to create various AI models and products.

  Score: 48🌐 MovesJul 10, 2026https://www.govtech.com/education/higher-ed/syracuse-university-to-offer-new-ai-degrees-to-boost-enrollment
• The many controversies of Meta’s AI glasses

  Meta says its AI glasses are an “assistant that understands the world from your perspective.” Critics say they’re “even more privacy invasive than you think.” One thing both parties can agree upon, though, is that these smart glasses are a technology that has attracted all manner of controversy. Since the 2023 release of the Ray-Ban Meta, these smart lenses have divided people. Evangelists praise the ability to take photos and videos without having to dig out their phone, as well as the navigational assistance. Opponents point to the company’s less than impressive track record with privacy and say the glasses opens a huge number of issues around tracking, consent, and facial recognition The fight over Meta glasses isn’t slowing down, with the most recent story seeing courts in New York state ban the technology to stop filming of legal proceedings. Here’s a look at some of the biggest points of contention the company has faced. Covert recording By far, the most controversial aspects of Meta glasses center on its embedded camera, which can be used to take pictures or video of others without permission. Given that some users leave the camera on all the time, The Electronic Frontier Foundation points out that the camera could capture someone entering their passcode or password into their phone, computer, or an ATM. The glasses have a small indicator light shows when the glasses are recording video footage, but there has been a robust black market for workarounds that disable this feature for quite some time. Meta responded on July 7, 2026, updating the produt FAQ to say the glasses’ camera will be disabled when users tamper with or destroy the recording LED. The company rolled out a mandatory update to enable that feature—and began removing ads, posts and Marketplace listings for services that offer workarounds to the LED light. In a statement, Meta spokesperson Dina El-Kassaby wrote that “The people who use [Meta Glasses] and those around them need to trust them. That’s why we built privacy into our AI glasses from the ground up. For example, every pair has a capture LED that blinks when you take a photo or video that you can save or share, it can’t be turned off, and if someone covers or damages the LED, the camera is disabled. We will keep strengthening our protections as our glasses become even more capable.” Private data. Human review. Meta found itself facing a class action lawsuit in March over reports that human workers review footage from Meta glasses, including content that includes nudity, people having sex, and using the toilet. That would seemingly run counter to the marketing Meta used, which included phrases like “designed for privacy, controlled by you.” Meta’s terms of service for its AI, which includes the Meta Glasses, does include a line that it preserves the right to review interactions with AIs and “may do so through automated or manual (i.e. human) review and through third-party vendors in some instances.” Facial recognition Just over a month ago, Meta was found to have quietly embedded face-recognition software into the Meta AI app, which is used in conjunction with the glasses. That came just two months after the company said it wouldn’t roll out facial recognition without taking “a very thoughtful approach.” The code has not yet been enabled by the company. The blowback from privacy advocates was intense. Meta CTO Andrew Bosworth, in a recent interview , said facial recognition would be used to identify people they know, not one that pulls data from a central database. As with the LED light, though, there are unofficial ways to enable facial recognition even if Meta decides to remove that code. In March, a cybersecurity specialist paired the glasses with a commercially available facial recognition platform and was able to identify people and pull personal details about them in real time. The EFF has warned that the idea of adding a facial recognition functionality to the glasses “is a monumentally bad idea that should be abandoned by Meta and any of its competitors considering a similar feature. But regardless of whether they launch this feature, it’s a pretty clear indication of where Meta wants these sorts of devices to go.” Banned from courts As a result of privacy concerns over the embedded camera, New York state will begin banning Meta glasses (and all forms of smart glasses) from courtrooms starting July 20. It’s hardly the first state to do so. Pennsylvania, Hawaii, and Wisconsin have banned them in some court systems, but New York new policy is the first to cover all courtrooms in an entire state. “The reason for this prohibition is to ensure that individuals cannot surreptitiously record court proceedings in violation of the New York State Civil Rights Law and applicable court rules,” read an internal memo from the New York State Unified Court System. Paywalling on-device features Earlier this month, Meta began restricting the Conversation Focus feature on its smart glasses, which boosts nearby voices in noisy environments. Previously, this was included with glasses, but now users only get three hours of that feature each month. Beyond that (and up to 15 hours) will cost $20 per month, part of the Meta One Premium program. That undercuts consumer expectations and is the latest example of tech companies attempting to monetize features that were once free. The backlash on social media sites, like Reddit , has been swift and furious. Consent laws As of the end of last year, 12 states, including California, Florida, Illinois, Maryland, Massachusetts, Connecticut, Montana, New Hampshire, Pennsylvania, and Washington, require all parties involved in a conversation to consent to audio recording of private communications. Because Meta’s glasses can capture audio passively, that raises legal questions about whether wearers are liable—and could end up with them facing criminal penalties, including potential jail time . Among the other questions: Do bystanders meaningfully consent to being recorded? And can businesses prohibit the devices? “The fact that many AI glasses lack obvious recording indicators—or have only tiny LED lights that are easily missed—compounds the risk,” wrote Joseph J. Lazzarotti, an attorney at JacksonLewis. “AI-generated transcripts created without consent or even awareness raise a myriad of issues.”

  Score: 48🌐 MovesJul 10, 2026https://www.fastcompany.com/91571430/the-many-controversies-of-metas-ai-glasses?partner=rss&utm_source=rss&utm_medium=feed&utm_campaign=rss+fastcompany&utm_content=rss
• Fort Worth officials call for data center moratorium as city weighs regulations

  Five Fort Worth City Council members want a moratorium on new data center development in the city, after months of public discussions that have highlighted a deep divide on the matter. Learn more about the regulations being discussed on the west side of the Metroplex in this story.

  Score: 48🌐 MovesJul 10, 2026https://www.bizjournals.com/dallas/news/2026/07/09/data-center-moratorium-fort-worth.html?ana=brss_6150
• Tencent is reportedly in talks to acquire Manus from Meta, following Beijing intervention — company expects to remain independent of Chinese tech giant

  Tencent is in talks with Manus and other investors to raise the $2 billion needed to buy back the startup from Meta. Beijing ordered the two companies to unwind the deal six months after the surprise announcement of its purchase.

  Score: 48💰 MoneyJul 10, 2026https://www.tomshardware.com/tech-industry/artificial-intelligence/tencent-is-reportedly-in-talks-to-acquire-manus-from-meta-following-beijing-intervention-company-expects-to-remain-independent-of-chinese-tech-giant
• Khalifa Fund launches second edition of ‘Prompt Engineering’ programme for members of Abu Dhabi Chamber Al Ain

  This is part of KFED’s continuous efforts to empower entrepreneurs with future skills and enhance their readiness to leverage AI technologies in developing projects

  Score: 48🌐 MovesJul 10, 2026https://www.zawya.com/en/press-release/companies-news/khalifa-fund-launches-second-edition-of-prompt-engineering-programme-for-members-of-abu-dhabi-chamber-al-ain-e2n99fa7
• How creativity, commerce and AI collide in mid-2026 marketing mix

  Mid-2026 has been a turning point for marketing. Technology, commerce and creative craft are jostling for equal billing, and the conversation has shifted from “what’s possible” to “what actually moves the business.” Below are the trends that are reshaping how brands plan, produce and prove marketing impact this year. AI moved from theory to practice […] The post How creativity, commerce and AI collide in mid-2026 marketing mix appeared first on e27 .

  Score: 48🌐 MovesJul 10, 2026https://e27.co/how-creativity-commerce-and-ai-collide-in-mid-2026-marketing-mix-20260709/
• Intuit’s Shape-Shifting Media; OpenAI Studies Abroad

  Intuitive Change 2026 has been an eventful year for Intuit’s media business on multiple fronts. In April, the company announced plans to shut down SMB MediaLabs, its ad network for small and local businesses. The idea behind SMB MediaLabs, which launched in 2023, was to retarget QuickBooks customers around the web. Then, just last month, […] The post Intuit’s Shape-Shifting Media; OpenAI Studies Abroad appeared first on AdExchanger .

  Score: 48🌐 MovesJul 10, 2026https://www.adexchanger.com/daily-news-roundup/friday-10072026/
• AI Is Taking Over This Crucial Part of the Recruiting Process: ‘It Was Very Realistic.’

  AI Is Taking Over This Crucial Part of the Recruiting Process: ‘It Was Very Realistic.’ Entrepreneur

  Score: 48🌐 MovesJul 10, 2026https://www.entrepreneur.com/business-news/ai-is-taking-over-this-crucial-part-of-the-recruiting-process
• Marketing’s new mandate: Why AI is forcing CMOs to think like ‘mini CEOs’

  Marketing’s new mandate: Why AI is forcing CMOs to think like ‘mini CEOs’ Fortune

  Score: 48🌐 MovesJul 10, 2026https://fortune.com/2026/07/10/marketing-new-mandate-why-ai-is-forcing-cmo-think-like-mini-ceo-monks-autodesk/
• How to automate product workflows with AI-powered video: A product manager’s guide

  How to automate product workflows with AI-powered video: A product manager’s guide Atlassian

  Score: 48🌐 MovesJul 10, 2026https://www.atlassian.com/blog/loom/ai-powered-video-guide-for-product-managers
• Operate like a Formula 1 team: The new AI operating model

  It is lap 47 of 57. Before the race began, the team had already processed gigabytes of race data, simulations, tire models, weather forecasts, competitor tendencies and scenario plans. But on the pit wall, there is tension. The race leader’s tires are degrading faster than predicted. A rival has just pitted for fresh tires and is closing the gap by three-tenths of a second per lap. The lead may not hold. In short, the race is not going to plan. A strategist now has only seconds to synthesize live telemetry, competitor data, weather projections, tire inventory, track position and race simulations into one call that could determine the outcome. They do not have those seconds because they are simply fast. They have them because the entire system behind the decision was designed that way: the data architecture, simulation models, communication protocols, decision rights, scenario playbooks and feedback loops all work together to compress complexity into a clear decision window. What if this is not just a racing story? What if it is also a blueprint for how the best enterprises will operate in the AI era? This builds on a broader shift I’ve described as the intent-driven future of work , where enterprise work begins less with navigating systems and more with expressing outcomes, context and intent. The AI advantage will not belong to companies with the most tools. It will belong to companies that redesign how work senses, decides, acts and learns. AI isn’t just a faster engine The popular story about Formula 1 is usually about speed or the quality of the driver . The fastest car with the most powerful engine with the driver with the quickest reflexes will win. But anyone who follows the sport closely knows that raw speed is only the starting point. Every car on the track is fast. Speed gets you into the race. It does not guarantee you a win. The teams that win consistently do so because of the quality of the system surrounding the car. They connect telemetry, simulations, strategy, engineering, pit operations, driver judgment and real-time learning into one high-performance operating model. Every part of that operating model matters. But the best individual part alone does not win the race. Enterprise AI strategy is at risk of making the same mistake that would keep an F1 team stuck in the middle of the pack: investing heavily in the engine while underinvesting in the entire race system. I see enterprising investing in more copilots, more agents, more dashboards, more tools and ultimately more automation. The AI systems perform their tasks at unprecedented speed. But the business outcomes do not change. In many ways, AI is becoming a new operating system of work not because it replaces every application, but because it changes how intent, context, workflow and execution come together. That is the gap many organizations are now facing. They have access to powerful AI capabilities, but they have not yet redesigned the operating model around those capabilities. The result is faster individual task execution inside disconnected systems, fragmented workflows and unclear accountability. In fact, a recent McKinsey report found that 88% use AI but two-thirds haven’t scaled it . The next phase of AI value will not come from simply adding more AI tools. It will come from redesigning how the enterprise senses, decides, acts and learns. The enterprise has too many disconnected signals Most enterprises do not suffer from a lack of signals. In fact, they are everywhere across the business. Customer intent signals, campaign performance data, product usage patterns, sales activity, support interactions, contract information, financial indicators, employee sentiment, security events and operational metrics already exist throughout an organization. The problem is signal fragmentation. The average knowledge worker has become the integration layer of the enterprise. They move between CRM, marketing automation, analytics dashboards, spreadsheets, collaboration tools, support systems, workflow platforms and financial reports. Then they manually assemble context that no single system provides. They do this to answer questions that should take seconds, not hours. Which customer needs attention? Which opportunity is at risk? Which process is slowing down execution? Which signal should trigger action? Which decision needs human judgment? In Formula 1 terms, this would be like a pit crew strategist having to call five different team members to gather tire degradation data, track conditions, competitor lap times, fuel load, weather forecasts and pit stop windows before making a race-defining call. The data exists. But the latency in accessing, interpreting and acting on it makes it less valuable at the moment of decision. That is the signal-to-action gap. And closing that gap is one of the most important opportunities in enterprise AI. The new operating model: Sense, decide, act, learn The AI-native enterprise needs to operate more like a Formula 1 team: continuously sensing, deciding, acting and learning. Sense is the foundation. It means connecting the right signals across systems, workflows, customers, employees and operations into a layer that AI can reason across. This is not just reporting on the past. It is creating the ability to understand what is happening now and anticipate what is likely to happen next. Decide is where AI intelligence and human judgment come together. AI can surface context, detect patterns, model options and recommend actions. Humans bring business judgment, ethical reasoning, organizational context and accountability. The quality of this partnership depends on the quality of the signals and context available to both. Act is where intelligence turns into execution. The goal is not another recommendation sitting in a dashboard. The goal is a workflow that triggers the right action, with the right controls, at the right time. Learn is where the operating model becomes a competitive advantage. Every action should generate feedback. Every outcome should improve the next recommendation. Every workflow should become smarter over time. In Formula 1, every lap creates learning. Tire wear, track temperature, driver feedback, competitor movement and weather changes continuously reshape strategy. The enterprise needs the same kind of learning loop. Semantic intelligence is the missing layer To close the signal-to-action gap, enterprises need more than data integration. They need semantic intelligence. Semantic intelligence is what helps AI understand enterprise meaning. It connects business language, customer context, workflow relationships, policies, roles, systems and outcomes so AI can reason across the business, not just retrieve information from systems. A customer health score is not just a number. Its meaning depends on product usage, renewal timing, support history, stakeholder engagement, commercial value, sentiment, implementation milestones and prior interventions. A delayed workflow is not just a status update. It may signal unclear ownership, missing approvals, poor handoffs, missing context, poor data quality or a decision that needs escalation. A sales opportunity at risk is not just a CRM field. It may reflect adoption gaps, customer sentiment, usage decline, executive sponsor changes, pricing friction, support issues or service delivery risk. Without semantic intelligence, AI can summarize what happened. With semantic intelligence, AI can understand what matters, why it matters, who needs to act and what action is most likely to improve the outcome. This is where enterprise AI value compounds. Foundation models will become broadly available. The model itself will not be the moat. The moat will be enterprise context, semantic intelligence, workflow intelligence, governance and learning loops. Redesign work before automating it There is a warning in the Formula 1 analogy that deserves attention: adding more power to a poorly designed system does not make it high performing. The same is true for enterprise AI. Adding AI to a broken workflow does not fix the workflow. It just compounds the dysfunction. If the data is fragmented, AI will produce incomplete recommendations confidently. If governance is disconnected from execution, AI can scale risk as quickly as it scales productivity. The question teams ask shouldn’t be, “Where can we insert AI into this existing process?” The better question is, “If we were designing this work from scratch, knowing what AI now makes possible, how should it operate?” This pushes leaders to clarify where work starts, what signals matter, which decisions should be automated, where human judgment is required, what controls must be embedded, how outcomes should be measured and how the system should learn. This is where CIOs, CTOs and technology leaders have an expanded role. AI transformation is no longer only about deploying technology. It is about redesigning how the enterprise works. Context becomes the differentiator In a world where every enterprise can access powerful models, context becomes the differentiator. The winning organizations will not be the ones with the most AI tools. They will be the ones with the strongest enterprise context and the clearest path from signal to action. That context includes customer history, product usage, workflow patterns, decision history, business rules, governance standards, risk boundaries, organizational knowledge and outcome feedback. It also includes knowing what happened after a decision was made. Did the action improve retention? Did it accelerate a deal? Did it reduce cycle time? Did it improve customer experience? Did it create risk? Did it scale? Without that feedback, AI remains a recommendation layer. With it, AI becomes part of a learning operating model. This is why the most important AI investments are not always the most visible ones. Data quality, identity, access, governance, workflow integration, observability, semantic models, feedback loops and change management may not sound as exciting as the latest AI agent. But they are what allow AI to create durable enterprise value. The CIO as architect of the race system The CIO’s role is evolving from technology operator to architect of the enterprise race system. That means connecting strategy, workflows, data, platforms, governance, security, talent and execution into an operating model that can move faster without losing control. The CIO’s job is no longer just to provide platforms. It is to design the conditions where intelligence can move safely and effectively through the enterprise with the right context, controls, accountability and feedback loops. Business teams need the ability to experiment and innovate. But they need to do so within clear standards for data access, identity, security, privacy, model usage, auditability, human oversight and business accountability. This is the balance every enterprise needs to strike: speed with control. The future is federated innovation with centralized guardrails. It is an enterprise operating model where more people can create value with AI, but within a trusted architecture that protects the company, the customer and the quality of decisions. The companies that pull ahead in the next decade will not be the ones that deployed AI first or assembled the largest portfolio of tools. They will be the ones who built the enterprise equivalent of a winning Formula 1 race system: a connected operating model. In Formula 1, the gap between the team that wins the championship and the team that finishes fourth is often measured in tenths of a second per lap. Compounded over a race distance, those tenths become decisive. The same dynamic is emerging in enterprise AI. This article is published as part of the Foundry Expert Contributor Network. Want to join?

  Score: 48🌐 MovesJul 10, 2026https://www.cio.com/article/4195140/operate-like-a-formula-1-team-the-new-ai-operating-model.html
• Introducing OpenWiki Brains, general-purpose wiki memory for agents

  A new general-purpose wiki memory system for agents, enhancing knowledge retrieval and context management.

  Score: 48🌐 MovesJul 10, 2026https://blog.langchain.dev/blog/introducing-openwiki-brains-general-purpose-wiki-memory-for-agents
• ‘Represent us’: Tensions flare over Taylor data center growth

  ‘Represent us’: Tensions flare over Taylor data center growth Austin American-Statesman

  Score: 47🌐 MovesJul 10, 2026https://www.statesman.com/business/technology/article/taylor-data-center-petition-texas-law-22333921.php
• India's TCS up as AI momentum fuels revenue beat; sector recovery gradual, analysts say

  India's TCS up as AI momentum fuels revenue beat; sector recovery gradual, analysts say Reuters

  Score: 46🌐 MovesJul 10, 2026https://www.reuters.com/world/india/indias-tcs-rises-after-quarterly-revenue-beat-2026-07-10/
• Humanitarians look to put the AI in aid

  Humanitarians look to put the AI in aid The Straits Times

  Score: 46🌐 MovesJul 10, 2026https://www.straitstimes.com/world/humanitarians-look-to-put-the-ai-in-aid
• Why AI could be a financial ‘sludge’ buster

  Regulators want to use the technology in their bid to cut red tape

  Score: 46🌐 MovesJul 10, 2026https://www.ft.com/content/da32dec7-bec6-483a-9595-18a3e9575a3f
• How to Build an AI Phone Agent with Twilio Conversation Relay in C Sharp

  How to Build an AI Phone Agent with Twilio Conversation Relay in C Sharp

  Score: 45🌐 MovesJul 10, 2026https://www.twilio.com/en-us/blog/developers/tutorials/product/ai-phone-agent-twilio-conversation-relay-csharp
• Malaysia PM Anwar to debut an AI double that sounds just like him

  Malaysia PM Anwar to debut an AI double that sounds just like him The Straits Times

  Score: 45🌐 MovesJul 10, 2026https://www.straitstimes.com/asia/se-asia/malaysia-pm-anwar-to-debut-an-ai-double-that-sounds-just-like-him?ref
• Change your thinking about AI ROI

  Change your thinking about AI ROI

  Score: 45🌐 MovesJul 10, 2026https://www.zoom.com/en/blog/unlocking-ai-roi-rethink-value-drive-results/
• AI agents for marketing: What they are, benefits, and examples

  I've always wanted a little robot helper of my own. Not the kind that automatically vacuums your floor and terrifies your dog. More like the one from Bicentennial Man (without the existential crisis and tears). That's what AI agents are: software teammates that can figure out and execute the steps needed to achieve a task—and talk to each other while they're at it. For marketers juggling campaigns, copy, and analytics across a dozen tools, AI agents for marketing are shifting how work gets done

  Score: 45🌐 MovesJul 10, 2026https://zapier.com/blog/ai-agents-for-marketing
• How To Build An AI Phone Agent With Twilio Conversation Relay in Node.js

  Learn to build a voice AI agent with Node.js, Twilio's Conversation Relay, and OpenAI. Create low-latency, natural phone support with real-time tool calling.

  Score: 45🌐 MovesJul 10, 2026https://www.twilio.com/en-us/blog/developers/tutorials/product/ai-phone-agent-twilio-conversation-relay-node
• AI for Product Management Course

  AI for Product Management Course GovTech Digital Academy

  Score: 45🌐 MovesJul 10, 2026https://www.thedigitalacademy.tech.gov.sg/course/detail/ai-for-product-management-course
• Khalifa Fund launches AI training programme for entrepreneurs in Al Ain

  Khalifa Fund launches AI training programme for entrepreneurs in Al Ain Gulf News

  Score: 45🌐 MovesJul 10, 2026https://gulfnews.com/uae/khalifa-fund-launches-ai-training-programme-for-entrepreneurs-in-al-ain-1.500603936
• How close is Rovo Dev to fully autonomous ticket resolution in real-world projects?

  How close is Rovo Dev to fully autonomous ticket resolution in real-world projects? Atlassian Community

  Score: 45🌐 MovesJul 10, 2026https://community.atlassian.com/forums/Rovo-questions/How-close-is-Rovo-Dev-to-fully-autonomous-ticket-resolution-in/qaq-p/3260272
• MTN Foundation, Microsoft empower Nigerian educators with AI integration skills

  Olayanju walked participants through a broad ecosystem of AI tools available to educators, including chatbots for lesson planning

  Score: 45🌐 MovesJul 10, 2026https://www.zawya.com/en/economy/africa/mtn-foundation-microsoft-empower-nigerian-educators-with-ai-integration-skills-x9qa5b5v