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Almost everyone is using artificial intelligence (AI) in their business, but very few have truly adopted it. The widespread adoption of AI has led many organizations to conflate usage with adoption, wondering why they’re not seeing the return on investment (ROI) expected by 2026. According to McKinsey, a staggering 88% of organizations now use AI in at least one business function, yet only 7% have fully scaled it. This is an adoption problem, not a usage issue. Adoption won’t show up in usage metrics; instead, it will be reflected in the quarterly business review.

HitPaw Edimakor is marking its second anniversary with a major promotion, offering creators around the globe up to 60% off on VIP plans and free gifts with selected purchases. The limited-time sale provides an opportunity for content makers to access Edimakor’s powerful AI tools at exceptional prices.

The platform has gained popularity among creators due to its comprehensive suite of features, which simplify every stage of content creation. Unlike traditional video editors, Edimakor is an all-in-one AI creative platform that enables users to generate, edit, and export professional content in one place.

Edimakor’s AI capabilities are impressive, with several advanced tools at their disposal. These include the AI Image Generator powered by Seedream 5.0 Pro, NanoBanana Pro, and GPT-Image 2 for creating high-quality images from simple prompts. The platform also features an AI Video Generator integrated with various AI models for producing cinematic videos.

Additionally, Edimakor offers AI Text-to-Speech with more than 1,000 natural voices across 80+ languages, as well as an AI Subtitle Generator that automatically creates and translates subtitles into over 130 languages. The platform also includes an AI Music Generator that turns text, poems, or creative ideas into original music.

The Easy Video Editor is another key feature of Edimakor, providing drag-and-drop editing tools that make professional video creation simple for beginners and experienced creators alike. This versatility makes it an attractive option for businesses looking to leverage AI tools in their content creation process.

Edimakor’s anniversary promotion includes several exciting deals for creators. The platform is offering 60% off on VIP plans, with a perpetual plan available for $55.95, including 10,000 lifetime AI credits plus 2,000 bonus credits. A one-year plan can be purchased for $39.99, including 3,000 annual AI credits plus 2,000 bonus credits.

AI Credits are also discounted during the anniversary sale, with up to 65% off on packages of varying sizes. For instance, a package of 100,000 credits is available for $203.99 (1-year subscription), while 10,000 credits can be purchased for $23.99 (1-month subscription).

Another exclusive offer includes the Edimakor Screen Recorder, which will be given away free to customers who purchase any Edimakor Video Editor Perpetual plan during the anniversary event.

The limited-time offers are a great opportunity for creators to access professional AI tools at outstanding prices. With its comprehensive suite of features and user-friendly interface, Edimakor is an attractive option for businesses looking to streamline their content creation process using AI technology.

Edimakor’s 2nd anniversary sale won’t last forever, so it’s essential for creators to visit the platform today and claim their exclusive savings. By doing so, they can start creating their next project with the latest AI tools at a fraction of the cost.

A major breakthrough in cell therapy has been announced, as Cellares and Sonoma Biotherapeutics are working together to automate the manufacturing process for SBT-77-7101, an engineered regulatory T cell (Treg) therapy. This treatment is currently undergoing Phase I clinical trials for patients with poly-refractory rheumatoid arthritis (RA), who have exhausted all available options.

Sonoma Biotherapeutics has developed a highly advanced Treg program that’s being translated onto Cellares’ commercial-scale Smart Factory platform. The company operates its first such facility in Bridgewater, New Jersey, and is building more sites in Europe and Japan to meet growing demand.

‘Tregs are uniquely sensitive to the manufacturing process,’ says Stephen Dilly, PhD, president, CEO, and board chair of Sonoma Biotherapeutics. He believes that Cellares’ platform will help his company deliver on its clinical ambitions at scale for patients with RA. The partnership aims to make sure every batch is reliable.

Fabian Gerlinghaus, co-founder and CEO of Cellares, notes that ‘every new cell therapy modality we bring to the Cell Shuttle and Cell Q expands what’s possible for the field and for patients in need.’ He emphasizes his company’s commitment to partnering with other organizations to advance innovative therapies.

The partnership is a crucial step towards automating Treg cell therapy manufacturing. Sonoma Biotherapeutics’ SBT-77-7101 has shown promise as a treatment option for patients with RA, and this collaboration aims to bring it closer to clinical success.

Dexory’s Todd Boone, VP of North America, reflects on the recent Automate 2026 conference, highlighting a notable trend among exhibitors. In addition to established vendors, many new market entrants showcased innovative solutions that could potentially disrupt traditional approaches. However, some of these newcomers appear to be targeting similar areas within the industry, which may lead to consolidation in the near future.

Generative artificial intelligence (AI) has become a ubiquitous presence in modern technology, but its resource-intensive nature is causing significant concerns within the industry and among consumers. The rapid growth of large language models such as ChatGPT and Claude has led to an unprecedented demand for high-end computer memory, resulting in a global shortage that is driving up prices. According to estimates, tech companies may be purchasing 70% of the world’s supply of high-end computer memory, causing a ripple effect on the market.

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.