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CNBC’s Jim Cramer has weighed in on the current state of the stock market, dismissing concerns that it is experiencing a bubble similar to the one preceding the dot-com crash. According to Cramer, companies like SpaceX are exceptions rather than representative of the broader market.

While some investors have raised eyebrows at the rapid gains made by semiconductor and AI-related companies, Cramer argues that these outliers do not reflect the overall market conditions. He points out that there is ‘some froth’ in certain areas but emphasizes that this does not accurately represent what drives the market’s performance.

The past year has seen significant stock price increases as enthusiasm for artificial intelligence continues to grow. Companies like Micron and Sandisk have experienced massive gains, with their shares rising by over 243% and 644%, respectively. This surge in prices has led some investors to question whether the market is becoming overheated, drawing comparisons to the dot-com boom of the late 1990s.

Cramer disagrees with this assessment, citing lower interest rates as a key factor contributing to the current market conditions. He also notes that corporate earnings are stronger than they were during the tech bubble and points out that valuations are more reasonable compared to those seen in the past.

The latest consumer price index report came in below expectations on Tuesday, which has eased concerns about potential rate hikes by the Federal Reserve. Cramer believes this development reduces the likelihood of a series of significant interest rate increases, similar to what occurred before the dot-com crash.

New Fed Chair Kevin Warsh’s comments on Tuesday also provided reassurance for investors. According to Cramer, Warsh did not indicate that he would tighten monetary policy if inflation remains at current levels. This suggests that the market is unlikely to experience a sharp correction in the near future.

Cramer also draws attention to valuations of major companies trading at what he considers attractive multiples despite reporting strong results. Bank of America, Goldman Sachs, and JPMorgan all reported substantial earnings and revenue beats on Tuesday, with their shares trading at roughly 12-18 times forward earnings. Cramer’s Charitable Trust owns shares in these companies.

These valuations are significantly lower than those seen during the peak of the dot-com era, when the S&P 500 traded at over 25 times forward earnings. According to FactSet data, this multiple is now around 20, which Cramer considers more reasonable but not cheap by any means.

Cramer also points out that several technology companies are trading at relatively low multiples compared to their historical norms. SK Hynix and Micron have shares priced at roughly four and six times forward earnings estimates for 2027, respectively. Nvidia’s multiple is similar to the broader market despite its dominant position in artificial intelligence.

What characterizes this market, according to Cramer, is the relatively low valuation of many large-cap stocks.

Artificial intelligence (AI) assistants are struggling to find relevant data across thousands of enterprise tables and unstructured documents, a common challenge faced by many teams when scaling large language model applications. This issue arises from the difficulty in navigating complex data landscapes, where raw schemas lack semantic relationships and business context that models need to reason effectively.

Pattern recognition is one of the most powerful tools humans possess. It’s a fundamental aspect of our cognitive structure, allowing us to quickly respond to threats and identify patterns in data. However, this ability also makes us prone to errors, as seen in pareidolia – the phenomenon of seeing patterns that aren’t really there. We’ve all been guilty of it at some point: seeing faces in rocks or finding meaning in song lyrics when none exists.

Artificial intelligence (AI) relies heavily on pattern recognition, using machine learning methods to power through vast amounts of data and identify significant patterns. But new research suggests that AI’s pattern recognition abilities are not foolproof – they can be easily fooled by out-of-distribution samples. This is particularly concerning when it comes to detecting life beyond Earth.

The study, titled ‘Can AI Detect Life? Lessons from Artificial Life,’ was conducted by Ankit Gupta and Christoph Adami from Michigan State University. They used the Avida Digital Evolution Platform (Avida) – an artificial life software platform that allows researchers to create digital organisms and study evolutionary biology. The researchers generated tens of thousands of digital organisms, some containing instructions for self-replication and others not.

Their goal was to train a neural network to recognize whether these digital organisms were living or non-living based on their molecular structure. They achieved an impressive 99.7% accuracy with the initial sample set. However, when they introduced out-of-distribution samples – molecules that weren’t part of the original dataset – the AI became confused and began misclassifying them as living.

The researchers found that it took as few as 150 tweaks to the code for the AI to confidently proclaim that a non-living organism was indeed alive. This is concerning, especially when considering future missions to Mars or other planets where AI will be used to detect signs of life. The likelihood of encountering out-of-distribution samples in these environments is substantial.

Christoph Adami, co-author and professor at Michigan State University’s departments of microbiology and molecular genetics, as well as physics and astronomy, emphasized the importance of human oversight when using AI for critical tasks like detecting life. ‘You need an independent way of checking their work,’ he said. ‘There needs to be a human in the loop.’ This is particularly challenging on space missions where communication with Earth can be delayed or unreliable.

The researchers’ next step will be to train their AI on real-world data and test its ability to detect life beyond what it’s been trained on. However, this study highlights a crucial vulnerability in current AI methods – their susceptibility to out-of-distribution high-confidence failures. This could have significant implications for future astrobiology missions.

The use of AI-generated images or LLMs as judges has become increasingly common in various fields, including data analysis and machine learning jobs. While these tools can be incredibly powerful, they are not infallible. As seen in this study, even the most advanced AI systems can be easily fooled by out-of-distribution samples.

The researchers conclude that if false positives outnumber true positives in extraterrestrial measurements due to AI’s propensity for being misled by out-of-distribution samples, we risk accepting high-confidence classifications at face value. This emphasizes the need for a fact-checker or human oversight when using AI for critical tasks like detecting life beyond Earth.

The study’s findings have significant implications for future astrobiology missions and highlight the importance of developing more robust data analysis tools that can handle out-of-distribution samples. As we continue to rely on AI in various fields, it’s essential to acknowledge its limitations and develop strategies to mitigate these vulnerabilities.

Greek robotics startup Acumino has secured a significant seed funding round, raising $11.7 million to accelerate the commercial deployment of its innovative physical AI platform for industrial automation.

The company’s technology enables robots to adapt across different hardware platforms without requiring task-specific re-programming for each robot type, driving new standards for flexibility and scalability in automation.

Acumino develops AI that trains robots to perform dexterous manipulation tasks in various industrial settings. Its data-driven approach allows for seamless integration with a range of robotic systems, streamlining the process of automating complex manufacturing processes.

The funding will support Acumino’s commercial expansion plans, including the growth of its engineering team and further development of its AI platform.

Acumino was also selected to participate in Google DeepMind’s European robotics accelerator program. The company is one of just 15 startups chosen for this prestigious initiative, which provides access to cutting-edge technology, cloud credits, and technical mentorship.

The participation in the DeepMind accelerator marks a significant milestone for Acumino, as it positions the startup as a leading player among European robotics companies. As the sole Greek participant, Acumino is set to benefit from increased visibility and credibility within the industry.

Orrick advised Acumino on the transaction, with George Pothoulakis leading the team that worked closely with the company’s leadership.

The financial advisory industry is built on relationships, guidance, and trust. However, many firms continue to spend an inordinate amount of time on administrative tasks that technology can now handle more efficiently.

A significant portion of this time is spent scheduling meetings, chasing paperwork, and manually updating client records. While these tasks may seem essential, they take away from the value advisors bring to their clients – building relationships, providing guidance, and instilling confidence in their financial futures.

Automation isn’t about replacing people; it’s about eliminating repetitive tasks so advisors can focus on higher-value activities that drive growth and deepen client connections.

If you’re looking to increase capacity without increasing headcount, consider automating the following five administrative tasks:

Scheduling meetings is one of the most time-consuming aspects of an advisor’s job. Coordinating calendars, confirming availability, sending reminders, and managing reschedules can consume hours each week.

Automated scheduling tools allow clients and prospects to book appointments based on your availability while automatically sending confirmations, reminders, and calendar invitations.

This not only reduces administrative workload but also leads to fewer scheduling conflicts, lower no-show rates, improved client experience, and faster prospect engagement. When scheduling becomes frictionless, clients receive faster service, and advisors reclaim valuable time.

Client onboarding is a critical process that sets the tone for future interactions. Unfortunately, many firms still rely on manual emails, paper forms, and multiple follow-up requests to onboard new clients.

A well-designed automated onboarding workflow can streamline everything from document collection and account opening to compliance requirements and welcome communications.

This includes automatically delivering welcome materials, collecting required documents, triggering account opening workflows, assigning internal tasks, tracking completion status, and scheduling follow-up meetings. Not only does automation improve efficiency but also creates a more professional and consistent experience for every new client.

Client relationship management systems are essential tools in an advisor’s arsenal. However, they’re only as valuable as the information they contain. Many advisors struggle to keep records updated due to manual data entry being time-consuming and often falling by the wayside.

Today’s integrations can automatically update contact information, log client interactions, record meeting notes, create follow-up tasks, track client milestones, and sync information across platforms.

This improves accuracy, enhances reporting, and ensures advisors always have access to the necessary information when serving clients. Automating data management is a crucial step in leveraging technology for business growth.

Routine client communications are essential for maintaining relationships but can quickly become overwhelming as firms grow. Automation allows firms to create personalized communication journeys that keep clients informed and engaged throughout the year.

This includes sending birthday and anniversary messages, quarterly review reminders, market commentary distributions, educational content campaigns, event invitations, and client survey requests. The result is a stronger client experience without adding significant administrative burden to your team.

Compliance remains one of the most important responsibilities within an advisory firm. While oversight should always involve human review, many routine compliance processes can be automated to improve consistency and reduce risk.

Automation can help with document routing and approvals, required disclosures, audit trail creation, workflow tracking, task reminders, and record retention processes. By reducing manual processes, firms can improve operational efficiency while maintaining the standards required in a highly regulated industry.

The purpose of automation isn’t simply to save time; it’s to create capacity for growth. Capacity to meet with more clients, develop your team, focus on strategic initiatives, or deliver a better client experience – these are just some benefits that come from streamlining administrative tasks.

As the wealth management industry continues to evolve, advisors who embrace automation will be better positioned to scale their businesses, improve operational efficiency, and focus on what matters most: helping clients achieve their goals. The future of advisor growth isn’t about working harder; it’s about building systems that allow you to work smarter.

A new framework to regulate the oversight of artificial intelligence (AI) has been proposed by Demis Hassabis, co-founder and CEO of Google’s DeepMind. In a blog post published on Tuesday, Hassabis emphasized the need for ‘urgent action’ in addressing potential risks associated with AI development. The focus is on frontier AI models, which are advanced, high-performing, and general-purpose systems that have human-level cognitive abilities.

Hassabis’ proposal comes just one day after Microsoft CEO Satya Nadella highlighted concerns about consumer data privacy and intellectual property theft through the use of AI in a post titled ‘The Reverse Information Paradox.’ This issue is particularly relevant when it comes to stealing trade secrets, which requires more robust data protection measures than traditional patent and intellectual property safeguards.

The proposed framework would establish a new Standards Body, modeled after federally overseen public-private partnerships or self-regulatory organizations like the Financial Industry Regulatory Authority (FINRA). The board of this body would comprise leading independent technical experts and open-source representatives. Funding for this initiative is expected to come primarily from industry sources, with substantial sums required to attract world-class talent and provide necessary computing resources.

The framework’s primary goal is to develop a system that can qualify as ‘Frontier-class’ by meeting certain criteria based on benchmarks determined by the Standards Body. These benchmarks would need to be regularly updated to keep pace with advancements in AI technology. Participating organizations with Frontier Models, defined by these benchmarks, would be designated as ‘Frontier Labs,’ and encouraged to adopt best practices such as publishing model cards with technical details and maintaining strong internal cybersecurity.

One of the key aspects of this framework is the voluntary sharing of models with the Standards Body for review up to 30 days before release. This process would help identify potential vulnerabilities and ensure that Frontier Labs adhere to established guidelines. The industry itself would set these guidelines, which could include requirements such as digitally watermarking AI-generated images and generating human-readable output tokens to understand model reasoning.

The proposed framework also envisions an ecosystem of third-party auditors working with the U.S. government to assess and develop new benchmarks for evaluating AI systems. Hassabis emphasized that this initiative is crucial in addressing potential risks, including cybersecurity threats and unknown issues that may emerge as capabilities continue to advance. He noted that ‘we’ve already seen the challenges frontier models pose for cybersecurity,’ and warned of more significant threats on the horizon.

Hassabis’ vision for a new age of AI development emphasizes the need for robust safeguards to maintain control over increasingly agentic, recursively self-improving systems. This requires not only technical expertise but also careful consideration of how AI is deployed for societal benefit. He emphasized that ‘the magnitude of this technology’s impact will be unprecedented,’ potentially 10 times greater than the Industrial Revolution at a speed 10 times faster.

The proposed framework would promote innovation while incentivizing responsibility and security, foster international collaboration on key safety issues, and encourage careful consideration of AI deployment for societal benefit. Hassabis concluded that ‘cautious optimism is the sensible and correct strategy’ during this time of significant uncertainty and high stakes.

Google DeepMind’s Demis Hassabis has proposed the creation of a new regulatory body to oversee the development and release of frontier artificial intelligence models. In an X post on Tuesday morning, he outlined his vision for a ‘standards body’ modeled after the Financial Industry Regulatory Authority (FINRA). This organization would be responsible for testing these advanced AI systems and establishing best practices for their deployment.

A highly exclusive and limited-run variant of the Tesla Model S Plaid has been listed for sale by a private owner in New Jersey. The vehicle, one of only 250 units produced as part of the Model S Signature Edition, is being sold through J&S Autohaus with an asking price of $260,490. This is significantly higher than the original retail price of $159,420 that Tesla charged earlier this year for the same model.

The Model S Signature Edition was a farewell variant of the Model S Plaid produced to mark the end of production of both the Model S and Model X flagship vehicles. It features top-tier performance combined with bespoke cosmetic and luxury upgrades, including the Luxe Package. The vehicle’s unique exterior design includes exclusive Garnet Red paint, gold Tesla ‘T’ badges upfront, and gold Plaid and Signature badging at the rear.

The interior of the Model S Signature Edition is equally impressive, featuring white Alcantara upholstery with gold piping/accents, gold Plaid seat badges, and a custom Signature key fob. The vehicle also boasts carbon-ceramic brakes with gold calipers, as well as four years of Premium Connectivity and free lifetime Supercharging through Tesla’s Luxe Package.

Tesla owners who purchase the Model S or Model X are subject to a no-resale clause that prohibits reselling for the first year. However, it remains to be seen whether Tesla will take action against the seller in this case. The company has stated that if ownership is transferred within the first year, the Full Self-Driving (Supervised) feature and other benefits will terminate.

The Model S Signature Edition was a highly exclusive offering, with limited invite-only sales available to select customers. It serves as a collector’s item celebrating the legacy of the Model S, which has been instrumental in Tesla’s electric vehicle success since its launch in 2012. The vehicle’s unique features and high price point make it an attractive option for collectors and enthusiasts.

Tesla has introduced a No Resale Agreement for the Signature Editions of the Model S and Model X, which would penalize the seller for ‘the amount of $50,000 or the value received as consideration for the sale or transfer, whichever is greater.’ This agreement aims to prevent unauthorized resale and maintain control over the vehicle’s ownership.

The company continues: ‘If you sell or otherwise transfer the ownership of your Model S or Model X, the remainder of the Recommended Maintenance, Wheel and Tire Protection Plan, and Windshield Protection Plan will transfer automatically to the buyer. The Full Self-Driving (Supervised), Free Supercharging and Premium Connectivity will not transfer with the vehicle and will terminate once the ownership of the Model S or Model X is transferred.’

A proposal has been put forward by Google DeepMind’s chief, Demis Hassabis, calling for a US-led body to test frontier artificial intelligence models. The proposed organization would cover both domestic and international developments in the field, including open and closed systems.

The aim of this initiative is to identify potential dangers and cybersecurity risks associated with these advanced AI models. It also seeks to determine whether existing safeguards can be bypassed, which could have significant implications for their deployment.

Hassabis has warned that artificial general intelligence capable of matching human brain function across a range of tasks may only be a few years away. This raises concerns about the need for oversight and regulation in this area, with society having a ‘precious window’ to establish effective controls.

The proposed framework would draw inspiration from existing regulatory bodies such as FINRA, which oversees US securities firms. Hassabis believes that a US-initiated system could become the foundation for international standards on AI testing and deployment.

A key advantage of this approach is that it would provide a common testing process for AI companies, avoiding the need for separate licensing regimes for each model. Independent testers would be able to compare models against established thresholds and update tests as new capabilities emerge.

The White House has already taken steps towards pre-release oversight, with an executive order issued in June seeking voluntary access to frontier models before their release. This move was followed by agreements from Google DeepMind, Microsoft, and xAI to provide models for federal national security testing.

However, the debate around AI regulation continues, with concerns raised over the recent restriction on access to Anthropic’s Mythos models due to safety control bypass risks. While Anthropic complied with this decision, they argued that it did not justify a broad recall of their models.

The US Congress is also considering additional requirements for frontier developers, including reporting dangerous capabilities and breaches within seven days of discovery. This move marks a shift from debating the need for oversight to deciding who conducts tests, which models qualify, and whether regulators can delay deployment.

A new development in the world of artificial intelligence (AI) is set to revolutionize how organizations manage their workforce. Betterworks, a leading provider of performance management solutions, has launched its Model Context Protocol (MCP) Server, enabling secure access to performance data for AI assistants such as ChatGPT and Microsoft Copilot.

The MCP capabilities allow these AI agents to securely access Betterworks’ performance data, helping organizations transition from reactive work to proactive execution. This marks a significant shift in the way businesses use AI tools for business, moving beyond mere response to prompts and towards more proactive decision-making.

According to Doug Dennerline, CEO of Betterworks, ‘the next generation of AI will understand what’s happening across your business and proactively surface the insights that need your attention.’ By providing trusted performance intelligence, leaders can spend less time searching for information and more time coaching their teams, making better decisions, and driving business results.

Many organizations already use AI assistants to prepare for performance reviews, summarize information, and identify workforce trends. However, until now, Betterworks data had to be manually gathered before these tools could deliver meaningful insights. This new development streamlines the process, allowing leaders, managers, and HR teams to simply ask natural-language questions about goals, recognition, team performance, and organizational insights directly from their AI assistant.

For example, a manager can ask: ‘Help me prepare for my 1:1 with Jordan, including goal progress…’ or ‘Which of my team goals are at risk this quarter?’ Instead of spending time gathering information, Betterworks helps managers prepare for coaching conversations, identify emerging risks, and generate leadership-ready updates. By making trusted performance context instantly accessible, organizations can move faster, respond sooner, and make more confident talent decisions.

Security remains a top priority with the MCP Server. Every interaction respects Betterworks’ existing authentication and native permission model, ensuring users only access the information they already have permission to see. Organizations can confidently extend sensitive performance data into AI workflows without introducing new access controls or compromising governance.

The initial release provides secure access to Betterworks goals, teams, users, recognition, and hashtags, with additional capabilities and use cases to be supported as releases follow throughout the year. As organizations increasingly adopt AI across the workplace, Betterworks is making trusted, current performance and talent intelligence available wherever decisions happen—helping leaders act with greater speed, confidence, and context.

The MCP Server is currently available in beta for customers, marking an important step towards integrating data analysis tools into everyday business operations.

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.