Meta has significantly expanded WhatsApp Business by introducing voice calling for large businesses, along with enhanced AI-powered features to improve customer engagement and support. Starting mid-July 2025, businesses can now initiate and receive voice calls within the same WhatsApp conversation thread, preserving chat history and providing a seamless communication experience without switching apps.

Previously, only small business accounts had access to voice chats, but now enterprise-level businesses can also make and receive calls via the WhatsApp Business API. This update enables real-time, personal support for inquiries, consultations, or urgent updates, enhancing the overall customer experience.

In addition to voice calls, Meta is exploring AI-powered voice agents that can automate customer service interactions on WhatsApp, using technologies from startups like Vapi, ElevenLabs, Coval, or Phonic. AI features are also being developed to deliver personalized product recommendations and enable businesses to follow up with customers directly through chat, potentially increasing sales and engagement.

Meta is also rolling out video calling and voice messaging capabilities for businesses soon, further enriching communication options on the platform. These updates are part of a broader effort to make WhatsApp a comprehensive business suite, integrating messaging, voice, video, AI, and marketing tools in one ecosystem.

Moreover, Meta is centralizing marketing and campaign management tools across WhatsApp, Facebook, and Instagram, allowing businesses to create unified ad campaigns and reach more customers efficiently.

Overall, these enhancements position WhatsApp Business as a powerful platform for companies to engage customers through voice, AI-driven automation, and unified marketing, supporting over 200 million monthly business users and 1.5 billion daily WhatsApp users worldwide.

OpenAI CEO Sam Altman has sharply criticized Meta for aggressively poaching AI talent from OpenAI, describing Meta’s recruitment tactics as “distasteful” and warning that they could lead to “very deep cultural problems” within the AI research community. In a leaked internal memo shared with OpenAI researchers, Altman framed the situation as a clash between “missionaries” (OpenAI’s purpose-driven researchers) and “mercenaries” (those drawn by Meta’s lucrative offers), emphasizing that OpenAI’s commitment to developing artificial general intelligence (AGI) with a strong mission focus will ultimately prevail over Meta’s approach.

Meta recently announced the creation of a new superintelligence team, Meta Superintelligence Labs (MSL), led by Alexandr Wang (ex-Scale AI) and Nat Friedman (ex-GitHub), and has recruited several senior AI researchers from OpenAI and other leading AI organizations. Despite offering extremely high compensation packages—bonuses up to $100 million and total pay reaching $300 million over four years—Altman noted that Meta has not succeeded in attracting OpenAI’s top scientists, having to reach far down their candidate list.

OpenAI’s chief research officer Mark Chen described the talent departures as feeling like “someone has broken into our home and taken something,” underscoring the emotional impact on OpenAI’s team. Altman, however, reassured staff by highlighting OpenAI’s unique culture and mission-driven focus as key advantages, and hinted at a review of compensation to retain talent.

Meta’s leadership responded by downplaying Altman’s criticism, with Meta executives asserting that they are successfully recruiting talent from OpenAI and continuing to push aggressively in the AI race. Meta CEO Mark Zuckerberg confirmed the company’s strong ambitions in AGI development through the new MSL team, signaling an intensifying competition between the two tech giants in artificial intelligence research.

The dispute centers on Meta’s aggressive hiring strategy, which Altman believes threatens the cultural integrity and mission focus of AI research at OpenAI, while Meta aims to accelerate its position in the AI field by assembling a top-tier team through high financial incentives.

Cloudflare has launched a new feature called Pay Per Crawl, currently in private beta, which allows website owners to charge AI bots a fee each time they scrape or “crawl” their site for data. This initiative fundamentally changes how AI companies access web content by requiring explicit permission and payment from AI crawlers before they can scrape websites protected by Cloudflare.

Key points about the Pay Per Crawl feature:

• Control for Website Owners: Website owners can choose to block AI crawlers entirely, allow them for free, or set a specific micropayment rate per crawl request. They can also apply different rates or permissions to individual AI crawlers.

• Verification and Security: Cloudflare uses public key cryptography (Ed25519 algorithm) to verify that AI crawlers are legitimate and have paid for access, preventing malicious actors from impersonating paying bots.

• Impact on AI Training: Since Cloudflare protects about 20% of global web traffic, this feature could significantly affect how AI models gather training data, as many sites will now require payment or explicit permission for scraping.

• Supporting Publishers: The move aims to protect original content creators from losing traffic and ad revenue due to AI bots scraping content without compensation. Major publishers and media companies like Condé Nast, TIME, The Associated Press, and others have already embraced this permission-based model.

• Future Plans: Cloudflare envisions evolving the model to allow dynamic pricing and more granular control over content access, potentially motivating AI companies to negotiate content deals tailored to their specific needs.

Cloudflare’s CEO Matthew Prince emphasized that this initiative is about safeguarding the future of a free and vibrant Internet by giving power back to content creators while still enabling AI innovation.

Cloudflare’s Pay Per Crawl is a pioneering marketplace and technical framework that enables websites to monetize AI crawling, ensuring that AI companies pay for the data they extract, thereby supporting sustainable content creation and fair compensation for publishers.

Grammarly has acquired Superhuman, an AI-powered email efficiency tool, as part of its strategic expansion beyond grammar correction into a broader AI-powered productivity suite. This acquisition aims to accelerate Grammarly’s evolution into a comprehensive productivity platform that integrates AI agents across multiple applications, with email as a critical communication surface.

Key points about the acquisition:

• Superhuman, known for its fast and efficient email platform, uses AI to help users compose emails, perform follow-ups, and facilitate team discussions. Its users reportedly send and reply to 72% more emails per hour compared to traditional platforms, and AI-powered email composition has grown fivefold in the past year.

• Grammarly CEO Shishir Mehrotra emphasized that Superhuman’s product, team, and brand will continue, with Superhuman’s CEO Rahul Vohra and over 100 employees joining Grammarly.

• The deal follows Grammarly’s recent $1 billion funding round led by General Catalyst, providing resources to expand its AI workplace tools beyond grammar checking.

• Grammarly plans to integrate Superhuman’s AI capabilities to build a network of AI agents that streamline workflows in email and potentially other productivity areas like calendaring and task tracking.

• This move positions Grammarly to compete with tech giants like Google and Microsoft, which are also embedding AI features in their productivity suites.

Financial details of the acquisition were not disclosed. Superhuman was last valued at $825 million in 2021 and generates about $35 million in annual revenue, while Grammarly serves over 40 million daily users with annual revenue exceeding $700 million.

Grammarly’s acquisition of Superhuman marks a significant step toward transforming from a grammar tool into a leading AI-driven productivity platform focused on enhancing professional communication and workflow efficiency.

Cursor, the company behind the popular AI coding editor, has launched a new web application that allows users to manage a network of AI coding agents directly from any web browser on desktop or mobile devices. This marks a significant expansion beyond Cursor’s original integrated development environment (IDE), enabling developers to assign tasks, monitor progress, and merge code changes via natural language commands without needing to open the desktop IDE.

Key features of the new web app include:

• Natural language task assignment: Users can instruct AI agents to write new features, fix bugs, or answer complex codebase questions simply by typing requests in the browser.

• Background autonomous agents: These AI systems work independently on coding tasks without continuous user supervision, running asynchronously in the background.

• Cross-device access: The app works on any desktop, tablet, or mobile browser and can be installed as a Progressive Web App (PWA) for a native-like experience on iOS and Android.

• Collaboration and code integration: Team members can review agent-generated diffs, create pull requests, and merge changes directly from the web interface, streamlining team workflows.

• Slack integration: Users can trigger agents and receive notifications through Slack by tagging @Cursor, further integrating AI coding assistance into developer communication channels.

The launch reflects Cursor’s goal to “remove the friction” in developer workflows by making AI coding assistance more accessible and flexible. Cursor’s platform is already widely adopted, with over half of the Fortune 500 companies using it, and the company recently reported over $500 million in annual recurring revenue. The web app aims to support the growing trend of AI handling an increasing share of coding tasks, with Cursor targeting AI to manage about 20% of coding work by 2026.

Cursor’s new web app empowers developers to manage AI coding agents anywhere, enhancing productivity and collaboration by extending AI-assisted coding beyond the traditional desktop IDE to a seamless, browser-based experience on all devices.

Elon Musk’s AI company, xAI, has successfully closed a $10 billion funding round, split evenly between $5 billion in debt (secured notes and term loans) and $5 billion in strategic equity investments, according to Morgan Stanley. This capital injection will be used to expand xAI’s AI infrastructure, including building one of the world’s largest data centers and advancing its flagship AI chatbot, Grok.

xAI has already deployed 200,000 GPUs at its Colossus supercomputer facility in Memphis, Tennessee, and plans to build a new facility housing 1 million GPUs near Memphis to support its AI development. The company aims to compete with major AI players like OpenAI and Anthropic, with Grok being integrated into Elon Musk’s social media platform X (formerly Twitter) to boost adoption.

This latest round follows a $6 billion raise in December 2024, bringing xAI’s total capital raised to about $17 billion. The funding round was oversubscribed and attracted prominent global debt investors, reflecting strong confidence in xAI’s vision and growth potential in the competitive generative AI market.

The $10 billion funding will significantly bolster xAI’s capacity to develop cutting-edge AI technologies and infrastructure to rival leading AI companies.

Meta has announced the establishment of Meta Superintelligence Labs (MSL), a new division dedicated to developing AI systems that surpass human intelligence. This initiative underscores Meta’s aggressive strategy to compete with industry leaders such as OpenAI and Google, marking a pivotal moment in the global race for AI supremacy.

Meta Superintelligence Labs is led by Alexandr Wang, the former CEO of Scale AI, who has been appointed as Meta’s inaugural Chief AI Officer. Wang oversees the development of foundation models, product teams, and research initiatives. Co-leading the product development and applied research efforts is Nat Friedman, former CEO of GitHub, bringing significant expertise to the division. This leadership duo positions MSL to drive innovation and accelerate Meta’s AI ambitions.

In June 2025, Meta made a strategic investment of $14.3 billion to acquire a 49% stake in Scale AI, valuing the company at $29 billion. This transaction represents Meta’s largest external investment to date. Beyond acquiring cutting-edge technology, the deal secured critical talent, including Wang, whose leadership is integral to MSL’s mission. This strategic acquisition highlights Meta’s focus on integrating both technological and human capital to strengthen its AI capabilities.

Meta’s commitment to AI extends beyond talent acquisition. The company plans to invest between $60 billion and $72 billion in AI development in 2025 alone, signaling a robust financial commitment to advancing its technological capabilities. This investment comes as Meta seeks to recover from setbacks, including delays with its Llama 4 model and prior losses of AI talent to competitors. The establishment of MSL and these substantial investments reflect CEO Mark Zuckerberg’s determination to position Meta as a leader in advanced AI development.

With the launch of Meta Superintelligence Labs, strategic investments in AI infrastructure, and an aggressive approach to talent acquisition, Meta is making a formidable push to redefine its role in the global AI landscape. By combining significant financial resources, cutting-edge technology, and top-tier expertise, Meta is intensifying competition in the AI race. The establishment of MSL not only underscores Meta’s ambition to lead in advanced AI but also signals the onset of a high-stakes battle for innovation and talent in the industry.

Google’s Gemini AI suite is poised to transform classroom teaching methods by providing educators with over 30 AI-powered tools that enhance personalization, efficiency, and engagement in teaching and learning.

Key changes include:

• Personalized Learning: Teachers can create custom AI tutors called “Gems,” which are AI assistants trained on specific class content to offer tailored academic support to students, helping them grasp topics more deeply. This enables more individualized attention within large classrooms.

• Streamlined Lesson Planning: Gemini can generate draft lesson plans, quizzes, rubrics, and suggest relevant videos based on grade level and topic, significantly reducing teachers’ administrative workload and freeing time for direct student interaction.

• Interactive Study Guides and Real-Time Feedback: Educators can build interactive study materials and provide real-time feedback and hints to students, fostering a more responsive and engaging learning environment.

• Enhanced Classroom Management: Through tools like ChromeOS Class Tools, teachers gain real-time control over student devices—pinning content, sharing digital workbooks instantly, and monitoring screens remotely—improving focus and accessibility.

• Support for Diverse Learning Styles: AI-powered reading aids, video question suggestions, and differentiated content creation help accommodate varied student needs and promote inclusivity.

• Data Privacy and Security: Google emphasizes secure and trusted AI use in classrooms, addressing educators’ concerns about data privacy and academic integrity.

Gemini shifts teaching from routine administrative tasks toward more interactive, personalized, and data-informed instruction, empowering educators to better engage students and adapt to their unique learning needs while maintaining classroom control and integrity.

Microsoft has recently unveiled a groundbreaking AI system called the Microsoft AI Diagnostic Orchestrator (MAI-DxO), which it claims can diagnose complex medical cases four times more accurately and more cost-effectively than experienced human doctors. This announcement marks a significant step toward what Microsoft terms “medical superintelligence”—an AI model that surpasses the diagnostic capabilities of the best human clinicians worldwide.

Key details about this development include:

MAI-DxO achieved an 85.5% diagnostic accuracy on a benchmark test involving 304 challenging case studies from the New England Journal of Medicine (NEJM), compared to roughly 20% accuracy by a group of experienced physicians. The AI also reduced diagnostic costs by about 20% by selecting less expensive tests and procedures. The system uses an “orchestrator” approach, simulating a panel of virtual physicians with different diagnostic specialties and approaches working collaboratively. It breaks down each case into a sequential diagnostic process, mimicking how doctors ask questions, order tests, and refine diagnoses step-by-step.

The AI health unit behind this innovation was formed by Mustafa Suleyman, CEO of Microsoft AI, who recruited top researchers from Google’s DeepMind lab. This team has developed the AI to handle some of the most complex diagnostic challenges, aiming to alleviate healthcare staffing shortages and reduce long waiting times. Microsoft sees this as a transformative advancement that could be integrated into consumer-facing AI products like Bing and Copilot, which already handle over 50 million health-related queries daily. The goal is to provide more accurate, timely, and cost-effective healthcare advice and support to billions of people worldwide.

While the results are promising, Microsoft acknowledges that MAI-DxO is not yet ready for real-world clinical deployment and requires further testing, especially in routine and everyday medical cases. Microsoft CEO Satya Nadella highlighted this achievement as a major leap toward precision and efficiency in healthcare AI, emphasizing the potential to revolutionize medical diagnostics and patient care and  a pioneering advance toward medical superintelligence, promising to enhance diagnostic accuracy, reduce costs, and transform healthcare delivery in the near future.

Meta has introduced a new AI-powered Message Summaries feature for WhatsApp, designed to help users quickly catch up on unread messages in individual and group chats. This optional feature uses Meta AI to generate concise, bulleted summaries of missed conversations, visible only to the user and not to other chat participants.

Key points about the feature:

• Privacy-focused: The summaries are created using Meta’s Private Processing technology, which ensures that neither Meta nor WhatsApp can access the message content or the generated summaries. The processing happens locally on the user’s device or within a secure cloud environment, preserving end-to-end encryption and privacy.

• User control: Message Summaries are disabled by default. Users can enable or disable them via WhatsApp settings under Settings > Chats > Private Processing. Advanced privacy settings allow users to specify which chats (personal or group) can use AI summaries.

• Current availability: The feature is initially rolling out in the United States with English language support, with plans to expand to more countries and languages later in 2025.

• How it works: When there are unread messages, a small icon appears in the chat. Tapping it provides a quick bulleted summary of the key points from those messages, saving users time without scrolling through long conversations.

• Background: This builds on earlier Meta AI integrations in WhatsApp, such as asking questions directly to Meta AI within chats and generating images. The new stack allows WhatsApp to privately access chat context to summarize messages or offer writing suggestions13.

Meta’s WhatsApp Message Summaries offer a private, AI-driven way to quickly understand unread messages, emphasizing user privacy and control, and currently available in the U.S. with plans for wider release.