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Senate Banking Committee Chair Tim Scott has expressed interest in hearing Federal Reserve Chairman Kevin Warsh discuss data centers and artificial intelligence during a scheduled appearance before the committee this week.

The conversation is set to take place as part of Warsh’s semiannual monetary policy report to Congress, but Scott hinted that he wants to explore broader topics beyond traditional Fed matters. When asked what he hopes to hear from Warsh on CNBC’s Squawk Box, Scott emphasized the importance of addressing artificial intelligence and data centers in South Carolina.

Specifically, Scott is concerned about the impact of data centers on local electricity and water usage. He noted that some parties in South Carolina are pushing for a ban on these facilities due to concerns over increased utility bills, which has sparked a national trend with various states implementing moratoriums on new data center development.

The issue extends beyond state borders, as Scott framed it within the context of global competition between the US and China. He believes that artificial intelligence will play a crucial role in determining which country emerges victorious, and he wants to ensure that the US is positioned for success by addressing its own challenges at home.

Scott’s comments suggest that he sees data centers and AI as critical components of America’s future competitiveness, particularly when it comes to innovation and economic growth. By engaging with Warsh on these topics, Scott aims to gain a deeper understanding of how to balance the benefits of technological advancement with concerns over resource usage and local impact.

Hachette Book Group, Cengage Learning, and Elsevier have filed a federal class action suit against Google over allegations that the tech giant has been using copyrighted literature to train its artificial intelligence platform Gemini. The publishers claim that Google scraped their works from online pirating sites and licensed databases without permission, then used them to generate AI-created copies of books and articles.

The lawsuit alleges that Google’s actions will ‘weaken the incentive to create’ by allowing the tech giant to profit from copyrighted materials without compensating authors or publishers. The suit charges Google with four counts of copyright infringement and claims that the company has assembled a vast trove of copyrighted materials through its Gemini AI system.

Google obtained the copyrighted material by scraping online pirating sites and licensed databases, including those used for its Google Books and Google Scholar platforms. However, it was specifically forbidden from copying these works for other purposes, yet allegedly did so many times over to train its multi-billion-dollar generative AI system.

The publishers claim that Google deployed a purpose-built service designed to generate content that creates direct substitutes for their original work. This includes allowing users to type requests into Gemini and receive full or large portions of text from novels and articles, as well as generating new books.

AI-generated works can then be sold, competing directly with the publishers’ material. The suit claims that these substitute works take multiple forms, including verbatim copies of entire works, replacement chapters of academic textbooks, summaries, alternative versions, and inferior knockoffs that copy creative elements of original works.

Gemini’s outputs are tailored to mimic the expressive elements and creative choices of specific authors, making it difficult for publishers or authors to compete with. For example, Gemini can generate a 100-page murder mystery in just 20 minutes for $0.39 – an unprecedented scale and speed that displaces legitimate sales of books and journal articles.

Internal documents cited in the suit show that Google employees noted the potential risks of using copyrighted materials without permission or compensation. Despite this, Google allegedly did so anyway, flagging internally that it was ‘highly problematic’ but proceeding with the use of these works to train its AI models.

The publishers claim that Google could have simply bought the rights to copy the work and paid them accordingly, making this a classic case of copyright infringement. While AI technology may be new, the legal principles at the center of this case are not – copyright law applies equally to all companies, including those using novel technologies like Gemini.

This lawsuit joins a growing number of publishers and authors suing artificial intelligence companies over similar copyright infringement claims. Hachette has also filed joint suits with McGraw-Hill and MacMillan Publishing against Meta’s AI in May, highlighting the increasing concern about AI-generated content competing directly with human-created works.

Demis Hassabis, the chief of Google’s artificial intelligence division and Nobel laureate, has called for the United States to spearhead a standards body that would oversee new AI models and assess national security risks. In an article posted on X, Hassabis emphasized the need for ‘urgent action’ to address the challenges associated with artificial general intelligence (AGI), which refers to the point at which AI matches or surpasses human intelligence.

Hassabis noted that frontier models have already posed significant cybersecurity threats, and other potential dangers such as nuclear and biological risks may soon emerge as capabilities continue to advance. He proposed a US-led public-private partnership overseen by the federal government as a solution to help tackle these threats. The White House, State Department, and Department of Commerce have been approached for comment.

The comments come on the heels of recent calls among industry leaders for an AI watchdog. Despite growing concerns over regulation, leading AI models are increasingly being subject to restrictions from public and private sectors alike. For instance, Anthropic was locked in negotiations with officials after the Trump administration temporarily imposed export controls over an advanced model, while OpenAI faced similar restrictions as it was initially requested by the US government to limit the rollout of a new model.

Hassabis argued that the US is well-positioned to lead in developing an AI framework ‘given its economic and technical standing.’ He suggested establishing a new Standards Body modeled on a federally overseen public-private partnership or self-regulatory organization, similar to the Financial Industry Regulatory Authority (FINRA), which regulates brokerage firms and exchange markets in the US. The proposed body would need substantial funding to attract world-class technical talent and provide necessary compute resources for large-scale testing.

Funding would likely come from industry, Hassabis said. Frontier labs would initially voluntarily share models with the Standards Body for review up to 30 days before release; after that, sharing would become mandatory for deployment in the US market if shown to be effective. The proposed body could also leverage specific agentic AI tests to identify attempts to bypass safety guardrails or signs of deception and ensure best practices such as digitally watermarking AI-generated images.

The calls for greater regulatory oversight come amid a heated competition between the US and China to develop and deploy AI models. Recent model releases from Chinese companies, including DeepSeek and Z.ai, are seen by many as highly competitive compared to leading frontier systems from Anthropic and OpenAI. As a result, US lawmakers are currently considering how to curb the growing adoption of Chinese AI models by homegrown companies, which raises ‘serious concerns’ according to the State Department.

Hassabis’s proposal for an AI standards body has been echoed by other industry leaders, including Anthropic CEO Dario Amodei and OpenAI’s Sam Altman. The two called for a US-led coalition to shape rules and standards around AI at a G7 meeting with tech leaders and heads of state that included President Donald Trump earlier this month.

The need for an AI watchdog has become increasingly pressing as the development and deployment of advanced models continue to accelerate. Hassabis emphasized the importance of addressing national security risks associated with AGI, which he believes requires ‘urgent action.’ The proposed Standards Body would provide a framework for overseeing new AI models and assessing potential dangers, ensuring that best practices are followed in developing and deploying these technologies.

Two major U.S. publishers, Cengage Group and Hachette Book Group, have filed a lawsuit against Google in federal court in New York, alleging massive copyright infringement behind its Gemini AI service. The suit was also joined by bestselling author Scott Turow. This latest development marks the second high-profile lawsuit against Google’s AI technology this year, following a similar suit filed by five major academic and trade publishers against Meta in May.

A proposal from Google DeepMind’s Demis Hassabis has sparked a significant conversation about the need for stricter regulation in the field of artificial intelligence. The plan, which would see a new regulatory body established to oversee the development and deployment of advanced AI models, carries weight across various sectors, including tech investing and cryptocurrency.

Hassabis, co-founder and CEO of Google DeepMind, has been advocating for greater oversight of AI development. In an interview with Axios, he outlined his vision for a regulator that would be funded by the industry itself, staffed by top technical talent, and ultimately accountable to Washington. This watchdog wouldn’t simply issue reports; it would have the authority to screen advanced models before release and coordinate an industry-wide slowdown if necessary.

The proposal is part of a broader effort to address concerns about AI safety. Hassabis has warned that ‘race conditions’ in AI development – where competitive pressure pushes labs to ship faster than they can verify safety – could lead to catastrophic consequences. He believes that pre-release testing requirements are essential, and companies should be required to prove their models are safe before releasing them to the public.

Hassabis’s proposal is not a sudden departure from his previous views on AI regulation. In 2026, he publicly expressed concerns about the risks associated with rapid AI development. His warning was prompted by the prospect of Artificial General Intelligence (AGI) emerging as early as 2029 – a milestone that would see AI match or exceed human-level reasoning across domains.

Hassabis’s call for greater regulation is significant because it comes from within the industry itself. As CEO of one of the world’s leading AI labs, he is essentially asking for constraints on his own company’s operations. This lends credibility to his proposal and underscores the need for a more systematic approach to AI regulation.

However, some have raised concerns that Hassabis’s plan could favor established players with deep pockets over smaller competitors. The proposed regulatory regime would require expensive pre-release testing and employ world-class technical reviewers – costs that startups and open-source developers might struggle to bear. This raises questions about the potential impact on innovation in the field.

Hassabis has emphasized the importance of global cooperation on AI safety, but his proposal is anchored in American governance. By doing so, he’s making a geopolitical bet: that the US is uniquely positioned to lead this effort due to its institutional credibility, technical talent pool, and market influence.

The past year and a half have seen an explosion of AI writing tools, but one pressing challenge has emerged alongside them: making machine-generated text sound like it came from a human being. As someone who spends their days editing and rewriting AI drafts, I’ve watched this space evolve from clunky outputs to something that often reads passably well – until you look closely. The telltale signs are still there: formal phrasing, repetitive sentence structures, and an overly polished tone in the wrong places. This is why I was curious to put Dr. Humanizer through its paces.

Siri, Apple’s virtual assistant, is set for a major overhaul as the company seeks to dominate the market for personal assistants. The revamped AI will leverage its massive user base and deep integration with personal data stored on devices to compete directly with ChatGPT and other top-tier AI assistants.

The key to Siri’s success lies in its ability to tap into users’ existing ecosystems, providing a seamless experience that rivals those of competitors. According to analysts at eMarketer, Apple’s scale is unmatched, with billions of active devices already in users’ hands. This extensive reach allows the company to infuse personalized AI features designed around its users.

Apple’s approach differs from the general free-for-all model employed by other companies. Instead, it focuses on creating a safe and highly personalized experience that leverages users’ trust. As analyst Gadjo Sevilla noted in a recent episode of ‘Behind the Numbers,’ Apple is not competing directly with ChatGPT but rather offering its own brand of AI features.

The upgraded Siri will be able to handle complex requests like adding photos to emails or showing specific images from user accounts. This level of functionality was demonstrated at Apple’s developer conference last month, where the company showcased the assistant’s capabilities in action. The revamped AI is expected to launch two years after initial promises and will incorporate Google’s Gemini AI model and cloud technology.

Apple’s control over both hardware and software provides opportunities for AI integration that third-party developers can’t replicate. However, this advantage initially extends only to Apple’s own apps and services, potentially limiting functionality with third-party applications like Gmail or WhatsApp. Analyst Grace Harmon noted that users who rely heavily on non-Apple services may find Siri’s capabilities limited.

Scale and trust are the two fundamental strengths positioning Apple favorably in the AI race. As eMarketer analyst Grace Harmon emphasized, ‘The two big things that are really working in Apple’s favor are scale and trust.’ With billions of active devices at its disposal, Apple has an almost unimaginable distribution network for AI features.

Apple is investing heavily in research and development (R&D) to support the revamped Siri. The company is using foundational models developed in combination with Gemini, as well as its own on-device processing capabilities. Analyst Gadjo Sevilla noted that Apple isn’t trying to reinvent the wheel but rather build upon existing AI technology.

The key differentiator for Siri AI lies in its ability to access and process personal information directly on users’ devices. This creates context awareness that competitors struggle to match, allowing Siri to provide a more personalized experience. Analyst Gadjo Sevilla explained that ‘A lot of what Siri AI is going to be able to do for its users securely will be on device.’ The assistant can pull answers from old message threads and extract information from emails without opening any apps.

Apple’s pitch centers around privacy, emphasizing the need for Siri to access messages, mail, and photos. The company claims it handles what it can directly on devices, with anything sent to the cloud used only to answer requests before being deleted. Analyst Grace Harmon noted that personalization is a crucial aspect of AI assistants, allowing users to tap into their existing data.

Despite its advantages, Apple faces several obstacles in the AI assistant race. The company has historically been criticized for delayed investment and limited third-party integration. As analyst Grace Harmon pointed out, ‘For a long time Apple has been kind of over promising, under delivering.’ This lack of urgency may hinder Apple’s ability to keep pace with competitors like Google or Microsoft.

Hardware limitations mean Siri AI won’t be available on all devices initially, potentially creating a fragmented user experience. Early demonstrations also suggest the assistant takes time to process requests, indicating it’s still in development ahead of its September launch. Third-party developers may face challenges accessing Siri AI integrations, which could further limit functionality.

The revamped Siri is set to put fully capable AI within reach of hundreds of millions of people who have never opened ChatGPT. However, users relying heavily on non-Apple services like Gmail or Google Photos may find Siri’s capabilities limited until those apps allow deeper integration.

The summer vacation season is a time for tech companies to think creatively about their workforces. With employees taking time off, it’s essential to keep the workflow running smoothly. For years, this has meant transferring urgent tasks between colleagues or creating overloads for those who remain in the office.

But 2026 marks a shift in approach as technology companies increasingly turn to advanced AI agents to support human teams and ensure business continuity. This change is significant, with many tech giants investing heavily in these digital assistants.

At WSC Sports, AI assistants are seen as an evolution of the digital workplace. The company’s internal pilot aims to evaluate the integration of AI agents into their organizational workflow. Shai Diament, VP of account management at WSC Sports, emphasizes that the ultimate goal is to ease employee burdens and improve professional quality of life.

The concept of AI assistants has transformed from its passive roots. Today’s advanced agents are proactive team members that listen, observe, and integrate seamlessly into organizational workflows. They can surface solutions, insights, and tasks independently in real-time without requiring human intervention.

‘One of the greatest contributions of this technology is allowing employees to truly disconnect during their vacations,’ Diament explains. By doing so, it serves as a vital support anchor for teams, maintaining a steady workflow even when employees are away on summer vacation or peak periods.’

The integration of AI agents is being tested in various roles and departments within WSC Sports’ internal environment. The company aims to closely monitor system performance and identify which positions and interactions deliver the highest value, responsiveness, and support to human teams managing it.

‘Our vision is to execute a broader roll-out,’ Diament says. ‘We want to expand these agents into additional roles across the organization and embed them in our daily operational infrastructure.’ This would alleviate employee workload and drive company-wide efficiency.

At SeatPick, another tech company, AI assistants are taking on a more tangible form with their agent named Naftali. Developed by global ticket resale price comparison platform SeatPick, Naftali assists human teams with both software development tasks and information gathering and analysis.

‘One of Naftali’s biggest advantages is helping us maintain business continuity during non-standard hours,’ Guy Kogel, CTO and co-founder at SeatPick, explains. This includes periods when the company experiences massive traffic due to events like World Cup matches or holidays.’

Naftali serves as a force multiplier by allowing existing workforces to remain productive and stress-free, especially during off-hours or peak periods. Kogel emphasizes that ‘he helps employees perform research and investigations much faster,’ enabling engineering teams to focus on higher-impact projects while other teams can move forward with improvements.

The integration of specialized AI agents is also critical in highly regulated fields like healthcare technology. At Navina, an intelligent agent named Ofir supports service teams for primary care physicians in the US. When a complex question arises regarding a medical recommendation made by the system, Ofir dives deep into full workflow documentation and analyzes calculation steps.

‘AI agents help bridge availability gaps during peak seasons or vacation periods,’ Shlomit Labin, VP AI at Navina explains. They shorten response times, reduce bottlenecks, and allow human teams to focus on truly complex cases while maintaining a continuous workflow and high quality of service.’

At Plarium, the focus is on creating a shared memory layer that acts as a bridge for the entire team. This ensures workflows build upon past achievements rather than repeating them. According to Tomer Daniel, AI product leader at Plarium, ‘it’s essential not to erase employees’ memories every night,’ just like you wouldn’t hire someone brilliant and then forget their work.

Daniel explains that most AI tools behave like this: every conversation starts from scratch with no idea what the team already knows. At Plarium, they are building Second Brain - a shared memory layer that turns documents, decisions, and hard-won lessons into knowledge its AI assistants can use.

‘When a company remembers, it stops reconstructing the past,’ Daniel notes. ‘Get to build on it.’ The potential impact of this technology could be significant for companies looking to streamline operations and improve employee productivity - we’ve seen early results from Plarium’s internal testing with Second Brain.

According to Labin at Navina, AI agents are crucial in fields like healthcare where maintaining business continuity is critical. With the help of AI tools, medical teams can focus on complex cases while ensuring a continuous workflow.

A federal judge in California just threw out a proposed class-action lawsuit accusing Google of secretly monitoring users’ private emails, chats, and video calls through its Gemini AI assistant. The ruling comes after the plaintiffs failed to provide enough detail about what information was accessed or how they were harmed by the alleged tracking.

The suit, filed by Thomas Thele and Melo Porter in November, accused Google of violating state and federal wiretapping statutes and invading users’ privacy when it switched on Gemini as a default feature for all Gmail, Chat, and Meet users in October 2025. Before that, users had to actively opt-in before Google’s AI could interact with their accounts.

Google argued that the plaintiffs never adequately alleged that their own data was touched or described a concrete enough injury to satisfy the standing requirements under Article III of the Constitution. The company pointed out that the plaintiffs failed to explain when they first signed up for Google’s services, whether they were already using Gmail, Chat, or Meet before Gemini was enabled by default on October 10, 2025.

The judge agreed with Google’s motion to dismiss the amended complaint, concluding that it failed to describe with sufficient detail the central harm it was built around: Google’s supposed intrusion into users’ private communications. The opinion highlighted several specific gaps in the complaint, including a lack of explanation about why October 10, 2025, was significant.

The judge noted that the plaintiffs never explained whether they were shown Google’s privacy policies when creating their accounts or confirmed whether Gemini’s features were already active at account setup. There was also no statement on whether users had since turned off the AI assistant’s features. The opinion stated that nothing in the complaint ruled out the possibility that Gemini had been running as a default feature well before October 2025.

Judge Wise found that the plaintiffs failed to show their own personal data was actually affected by Gemini, despite broadly describing sensitive financial, medical, and employment information that can live inside a Google account. The complaint didn’t point to any specific messages or communications that Gemini supposedly analyzed nor identify any particular personal data used by the AI tool.

The judge rejected the plaintiffs’ bid to pursue an order blocking Google’s practices going forward, finding they hadn’t shown they or other members of the proposed class faced an ongoing or future risk of the same harm. The court noted that users can eliminate any risk by switching off Gemini’s ‘smart’ features in their account settings.

Despite dismissing the complaint, Judge Wise gave the plaintiffs 21 days to file a new version addressing the shortcomings. Citing the general principle that courts should freely allow amended pleadings when doing so serves the interests of justice and helps resolve cases on their merits rather than procedural technicalities, the judge allowed the plaintiffs another chance.

The underlying complaint accused Google of violating California’s constitutional privacy protections and the state’s Invasion of Privacy Act, which bars secretly recording or intercepting confidential communications without consent. It also alleged violations of California’s computer data access law and the federal Stored Communications Act, both of which prohibit intentionally accessing protected electronic information without authorization.

Google argued that users can opt-out of Gemini’s features by switching them off in their account settings. The company pointed out that this option is available to all users who want to avoid having their private communications monitored or analyzed by the AI assistant. Users have control over how they use the Gemini tool and whether it interacts with their accounts.

The ruling comes as a significant development in the ongoing debate about artificial intelligence assistants and data analysis tools used for businesses. Concerns are being raised about user privacy and consent as more companies integrate AI into their services.

The Supreme Court recently ruled that police conducted a Fourth Amendment search when they obtained a robbery suspect’s Google Location History data through a geofence warrant. This decision strengthens privacy protections for cell-phone location records while leaving key questions about the warrant itself unresolved.

Amazon is facing a class-action lawsuit over claims its Ring doorbell cameras used facial recognition technology to scan, identify, and store people’s faces without consent. Similar concerns are being raised about user privacy in this case.

A group of Black employees who accused Google of maintaining disparities in hiring, pay, and promotions has reached a settlement with the company. The lawsuit alleged that Google engaged in a ‘pattern and practice’ of discrimination against its minority workers.

This case highlights the need for companies to be transparent about their use of AI tools and data analysis techniques. Companies like Google must consider the potential consequences of using these technologies without proper safeguards in place.

A Florida father has filed a federal lawsuit against Google alleging that its Gemini AI assistant contributed to events leading up to his son’s suicide and an alleged attempt to stage a violent incident near Miami International Airport. The lawsuit raises concerns about the potential consequences of using AI assistants without proper safeguards in place.

At the forefront of innovation in education, a significant development has taken place with the launch of ATL Saathi. This is a web application powered by Gemini, designed to provide educators at Atal Tinkering Labs (ATL) with a 24/7 planning and training assistant. The tool aims to transform these labs into AI-Augmented Discovery Labs, marking a crucial step towards leveraging technology in education.

The initiative builds upon the existing work of ATL, which has been providing access to new technologies such as 3D printing, IoT, and robotics for over 1.1 crore students across India. However, with this new development, the focus shifts from mere access to physical lab infrastructure to driving meaningful outcomes like accelerated innovation and enhanced learning metrics.

The collaboration between Atal Innovation Mission (AIM) and Google DeepMind has been instrumental in bringing about this change. During the AI Impact Summit in February 2026, it was announced that Google would help incorporate robotics and coding into local curricula, integrate Gemini thoughtfully into teacher workflows, and build a safely guardrailed AI assistant for students grounded in national curriculum standards.

According to experts, behind every good student is a great teacher. This understanding has guided the efforts of both AIM and Google in developing ATL Saathi. The tool’s primary goal is to support educators by streamlining their workload and providing them with the necessary tools and digital skills required for today’s classrooms.

The development process involved close collaboration between AIM, NITI Aayog, and Google. This ensured that the tool was grounded in teacher needs, accurately reflected ATL’s educational principles and pedagogy, and created genuine value for ATL educators. The result is a comprehensive platform designed to empower teachers and enhance student learning outcomes.

ATL Saathi boasts several key features that make it an invaluable resource for educators. Firstly, it provides streamlined onboarding and content curation through NotebookLM. This ensures that educators always have access to the most up-to-date content, including summarized modules, AI-generated infographics, video overviews, and interactive quizzes for 12 core modules from the ATL prescribed curriculum.

This micro-learning approach replaces lengthy videos, allowing teachers to quickly familiarize themselves with complex topics. Additionally, the tool offers advanced project generation capabilities through its ‘Push & Pull’ mentorship feature. For 10 core modules, teachers can access an interface that supports both generating project ideas and providing detailed experiments for students who bring their own problem statements.

Multilingual accessibility is another significant aspect of ATL Saathi. Educators can interact with the assistant in their preferred language, which responds and generates materials accordingly. Currently, the tool supports 8 languages, with flexibility to add more as needed.

The underlying intelligence powering ATL Saathi comes from Gemini, a model that creates concise instructional materials, including AI infographics, video overviews, and interactive quizzes for core curriculum modules. This helps teachers navigate complex training materials seamlessly and adopt micro-learning content efficiently.

Looking ahead, the initial rollout of ATL Sahti will involve 100 pilot schools across India. The aim is to see significant reductions in administrative load, higher efficiency, and an increased readiness among educators to help students tinker and innovate. By shifting the burden of administrative overhead and curriculum translation onto AI, educators can focus on what they do best: mentor, inspire, and guide.

The future holds promise for Indian education with ATL Saathi at its forefront. As technology continues to play a vital role in shaping educational outcomes, this tool is poised to make a significant impact.

The decline of Stack Overflow, a popular platform for software developers, has raised concerns about the future supply of high-quality training data. This issue is not just limited to one platform but may be indicative of a broader problem in the field of artificial intelligence (AI).

A recent study from the University of Auckland suggests that highly skilled contributors are increasingly disengaging from online communities as AI compresses the distinction between knowledge and AI-generated responses.

The trend is visible in Stack Overflow’s traffic, which has recorded a nearly 76% decline in monthly questions since ChatGPT’s launch in late 2022. Developers now increasingly turn to conversational AI for assistance, rather than seeking help from human experts on platforms like Stack Overflow.

This shift raises long-term questions about the future supply of high-quality training data. The departure of expert contributors from platforms like Stack Overflow may signal a bigger problem: the erosion of incentives that drive knowledge sharing among developers.

According to Dr. Kenny Ching, who led the research, AI-generated responses are becoming increasingly difficult to distinguish from those written by specialists. This phenomenon is described as ‘signal compression,’ where the value of human expertise diminishes when AI systems can generate comparable responses.

If everybody can create a good quality response or output using AI, some people may think, ‘Why should I make an effort to share my expertise and participate?’ Dr. Ching’s quote highlights the challenge facing organizations that rely on open platforms for knowledge sharing.

The implications extend beyond software development. Similar dynamics could emerge across classrooms, workplaces, research communities, and other collaborative environments where AI-generated content increasingly resembles expert work.

As the perceived value of expertise declines, organizations may find it harder to encourage meaningful knowledge sharing. This raises broader questions about AI’s own future development, particularly when it comes to training data.

Today’s large language models were trained on vast quantities of publicly available, human-generated knowledge, including content from communities such as Stack Overflow. If fewer experts continue contributing to open platforms, future training data may become increasingly fragmented or shift toward private collaboration channels.

This does not necessarily imply that future AI models will become less capable. However, it suggests that the open knowledge ecosystem that helped fuel the first generation of generative AI may be changing. As expert participation declines, maintaining high-quality public repositories of human knowledge could become as important as improving the next generation of AI models.

Developing artificial intelligence (AI) assistants that can perform complex tasks has been a long-standing challenge in the field. Recently, I embarked on a journey to build such an assistant using LangChain and LangGraph, two powerful tools for building AI agents. In this article, I’ll share my experiences and insights gained from this project, highlighting key patterns and lessons learned along the way.

Financial technology giant Revolut has teamed up with leading AI platforms to bring conversational trading to its users. The integration of its crypto exchange, Revolut X, with these AI assistants lets people trade and analyze their portfolios using natural language commands.

The list of integrated tools includes Anthropic’s Claude, Google’s Gemini, OpenClaw, and Cursor. These partnerships allow users to execute trades, backtest strategies, and set up custom alerts through simple conversational prompts.

Revolut X now supports a wide range of features that make it easier for traders to analyze the market and manage their portfolios. Users can place both market and limit orders, backtest strategies across over 300 tokens available on the platform, and even receive Telegram notifications when specific price targets are hit.

The technical underpinnings of this feature rely on Anthropic’s Model Context Protocol (MCP), which was first released in November 2024. This protocol is layered on top of Revolut X’s existing trading API to enable seamless integration with the AI assistants.

Revolut has also made available a dedicated GitHub repository that provides installation support for more than 50 AI applications. This move shows the company’s commitment to making its platform accessible and user-friendly for developers as well as users.

As before, all orders require user review and approval before execution, ensuring that no trade goes through without human confirmation. Revolut explicitly disclaims liability for any errors caused by AI-driven actions – they’re not taking on extra risk just because it’s a machine making the call.

The integration of these AI assistants with Revolut X comes hot on the heels of AIR, its in-app AI assistant for general financial tasks, which launched back in April 2026. This new feature is specifically designed for traders and operates at a deeper technical level than its predecessor, enabling users to tackle more complex trading strategies.

A group of around 200 protesters marched through San Francisco over the weekend, calling for a pause in the development of more powerful artificial intelligence (AI) models. The demonstration, organized by the Stop the AI Race coalition, targeted OpenAI, Anthropic, and Google DeepMind, with chants and signs emphasizing three key concerns: AI safety, job displacement, and environmental damage from data centers powering these frontier models.

The protesters’ message was clear: until someone figures out what they’re actually doing to society, stop training more capable AI systems. This is the second time this coalition has taken to the streets in 2026, with a similar march happening in March targeting the same companies and demands.

Organizers directly called on executives at OpenAI, Anthropic, and Google DeepMind to pause new AI system development, highlighting the need for greater transparency and accountability. The signs and chants centered around these three grievances, reflecting growing concerns over the impact of AI on society.

While the protest itself had nothing to do with cryptocurrency, the regulatory environment it reflects is influencing price action in a specific corner of the token market. Decentralized AI tokens, projects built around running AI inference outside company or government control, have been moving in response to AI policy news.

The U.S. government’s recent order restricting access to Anthropic’s models has had a notable impact on these tokens. Venice (VVV) gained approximately 14% following the announcement, while Morpheus (MOR) moved roughly 21%. However, these price swings are also a reminder that this market is highly reactive and thin.

The Stop the AI Race coalition appears to be gaining momentum, with renewed media attention and a clear message. The March protest generated some coverage but little policy response; it remains to be seen whether this latest demonstration will have more impact.

A new accelerator program aimed at supporting startups, research teams, and non-profits working on environmental challenges has been launched by Google DeepMind. The three-month AI for the Planet program will provide selected organizations with access to cutting-edge machine learning tools and expertise to tackle pressing issues such as climate change, sustainable agriculture, and nature protection in Asia Pacific.

The program is open to APAC-headquartered organizations that are working on projects involving artificial intelligence and environmental innovation. Applications are now being accepted until July 26th, 2026, with the selected teams set to join a three-month accelerator program starting from September this year. The program includes a week-long in-person bootcamp, virtual support through December, and an in-person Demo Day at the end of the program.

Google DeepMind will select between 10 to 15 organizations for the inaugural cohort, which will receive access to the Google AI stack, including specialized frontier models, as well as tailored support from Google experts. The program is designed to sit across multiple areas, including AI skills development, startup support, research commercialization, and environmental innovation.

The application criteria cover a range of projects, including those focused on climate change, sustainability, energy, nature protection, sustainable agriculture, and forest protection. Applicants must demonstrate a plan for integrating Google AI into their solutions, either through general models or specialized ones such as AlphaEarth, Forestry, or Perch. Additionally, selected teams will need to have established in-house technical capabilities in AI and machine learning.

Sanjay Jain, Head of Google for Education, India, highlighted the launch on LinkedIn, writing: ‘Got a big vision for the planet and the AI chops to back it up?’ The program is aimed at organizations with a functional prototype or minimum viable product, early validation or proven traction, and a roadmap where AI is central to their current solution or future technical development.

The accelerator will begin with an in-person bootcamp from September 7th to 11th, during which participants will meet Google mentors and define project goals through keynotes, workshops, and diagnostic sessions. After the bootcamp, selected organizations will receive three months of virtual support from September to December, guided by a dedicated relationship manager.

Participants will also have access to one-to-one mentorship and technical guidance from Google and industry experts, as well as tailored technical and business training. Selected startups or projects may be eligible for cloud credits through the Google for Startups Cloud program or Google Cloud for research, subject to eligibility review and approval. The accelerator is equity-free for the duration of the program.

The program will culminate in an in-person Demo Day in December 2026, where participating teams can present their AI-driven environmental work to mentors, investors, partners, and Google teams. Google DeepMind describes the program as a route for early-stage projects to move from experimentation into applied problem-solving: ‘Our mission is to use frontier AI to tackle the world’s most critical environmental challenges.’ The launch adds another AI accelerator route for APAC organizations working at the edge of research, climate technology, and applied AI development.

Applications for the first AI for the Planet cohort close on July 26th, 2026. Google DeepMind is also hosting virtual open forums during the application phase to provide more information about the program and answer questions from interested applicants.