Latest News

Ridgefield Park, New Jersey - August 18, 2026 - Communications teams can now derive decision-ready takeaways from verified media data in minutes without custom development required. This is made possible by the launch of the Agility MCP Connector, an extension of the company’s Media Intelligence API to AI assistants.

Agility PR Solutions has developed this connector as a solution for communications professionals who have adopted AI assistants for research, drafting, and analysis. However, these assistants typically answer from open-web data rather than media monitoring data that teams already rely on, resulting in a split screen between trusted data and assistant capabilities.

The Agility MCP Connector closes the gap by giving compatible AI assistants direct access to verified, structured media data through the Model Context Protocol (MCP), an open standard for connecting AI assistants securely to external data. This allows communicators to ask questions in plain language and receive insights grounded in indexed coverage rather than open-web results.

Agility’s Media Intelligence API provides programmatic access to a wide range of online, print, broadcast, podcast, radio, and social media sources, with over 2 billion licensed news items indexed annually. Until now, working with this data required building against Agility’s API or embedding it in dashboards, business intelligence tools, and internal applications.

The MCP Connector adds an alternative route to the same proprietary dataset, enabling teams to connect AI assistants directly to their media content without custom development. This streamlines access to verified media insights for communicators who work with AI assistants like Claude, which is compatible with the connector.

Once connected, communications teams can use the Agility MCP Connector to perform various tasks by interfacing with an AI assistant, including searching media coverage using natural language, generating executive takeaways, analyzing sentiment and trends, comparing brands and competitors, and visualizing insights such as share of voice and coverage over time.

The connector works securely through the Model Context Protocol (MCP), which allows an AI assistant to reach a specific data source instead of relying on open-web results. This ensures that compatible assistants can retrieve verified media coverage without custom API development or engineering effort.

Agility PR Solutions has built its platform for enterprise communications teams, delivering AI-native intelligence across multiple categories including media relations, social listening, and media monitoring. The company’s Media Intelligence API is recognized as a leader in the industry, with over 2 billion licensed news items indexed annually.

The Agility MCP Connector extends this capability to AI assistants like Claude, meeting communicators inside their tools of choice on data they can trust. According to Martin Lyster, CEO of Agility PR Solutions, ‘Our Media Intelligence API already delivers media insights our clients rely on. What they didn’t have was a way to put questions to it in the places they’re already working.’ The connector addresses this gap by integrating verified media data with AI assistants.

The Agility MCP Connector is available now for compatible AI assistants like Claude and can be found in the Claude Connector Directory, Anthropic’s reviewed catalog of connectors. Getting started requires an Agility account with Media Intelligence API access and a compatible MCP client such as Claude.

A significant milestone has been reached by Anthropic, a company behind the popular AI model Claude. According to sources familiar with the matter, the company’s annualized revenue run rate hit $65 billion at the end of July, marking a sevenfold increase from the same period last year. This figure was shared in an update to investors over the weekend, although details remain confidential.

Anthropic has seen its enterprise popularity surge, and this latest development is expected to bolster its case for a highly anticipated initial public offering (IPO). The company filed its prospectus with the Securities and Exchange Commission in June, but no official timeline has been provided for its debut. In preparation for the IPO, Anthropic has been holding preliminary meetings with potential investors.

Additionally, the company shared a preliminary revenue figure of $11.5 billion for the second quarter, representing a 14-fold jump from the same period last year. This growth is part of a broader trend in the AI industry, where companies are rapidly expanding their revenue streams. In comparison, chief rival OpenAI’s annualized revenue run rate recently reached $40 billion.

Anthropic has been navigating regulatory challenges, including a recent dispute with the government over export controls. The company was forced to temporarily disable access to two of its most advanced models, Claude Fable 5 and Mythos 5, in order to comply with an export control directive from national security authorities. After roughly two weeks of negotiations, Anthropic restored access to these models.

The resolution did little to ease concerns about Anthropic’s relationship with the Trump administration, which has been strained due to discussions around how the military could use its AI models. The company was blacklisted by the Pentagon earlier this year after talks spiralled out of control. In a June blog post, Anthropic expressed its commitment to government collaboration and thanked users for their patience during the disruption.

The company’s valuation remains high at $965 billion, and it is looking to demonstrate continued momentum to investors despite these challenges.

Travel industry network Virtuoso has introduced an artificial intelligence-powered foundation that aims to simplify complexity and enhance the human connection in luxury travel. The new capability, which was unveiled at Travel Week 2026, is designed to connect data, systems, and security across the Virtuoso network, making it easier for advisors, member agencies, and partners to access and act on expert knowledge and thousands of interconnected data points.

The foundation builds on Virtuoso’s existing technology philosophy, ‘automate the predictable so that you can humanize the exceptional.’ This approach has guided the development of various tech solutions aimed at preserving and enhancing the human connection in luxury travel. The new intelligence capability is a key component of this strategy, which seeks to streamline complexity and unlock collective advantage within the Virtuoso network.

Virtuoso’s advisors have been quick to adopt AI technology, with 74% now using it in their business. This adoption rate far surpasses the industry average, reported by Phocuswright at 59%. However, despite its benefits, AI-generated travel content has also revealed some limitations. Much of this content overlaps due to similar sources being used, resulting in repetitive and untailored itineraries that contribute to overcrowding at popular destinations.

Human expertise remains a crucial aspect of luxury travel, with three-quarters of Virtuoso clients citing their advisor’s accommodation and destination expertise as a key benefit. In contrast, fewer than half of travelers advised by others share this sentiment. Additionally, 60% of Virtuoso clients point to an advisor’s ability to curate experiences tailored to them, whereas only one-third of those advised elsewhere can say the same.

The luxury travel market is showing interest in using advisors for future trips, with 79% of high-end travelers expressing openness to this approach. Furthermore, half of Virtuoso advisors report clients steering away from over-touristed destinations, highlighting a growing desire for unique and exclusive experiences.

As AI technology continues to evolve, it’s clear that algorithmic inspiration is giving way to analog escapes in luxury travel. Time, privacy, and the ability to disconnect are emerging as the ultimate luxuries, with Virtuoso positioning itself at the forefront of this trend.

The new intelligence capability is built on Virtuoso’s proprietary global knowledge, which has been cultivated over decades through member and advisor expertise, partner relationships, and institutional experience. This collective knowledge was previously scattered across people, conversations, and systems that couldn’t be universally accessed. The foundation will simplify complexity by connecting data, systems, and security across the network.

According to Virtuoso Chairman/CEO Matthew Upchurch, ‘What we’ve built is a foundational digital capability that is ever evolving with infinite possibilities.’ This new capability brings together expertise, insights, data, and relationships within the Virtuoso community – advisors, member agencies, preferred partners – making it easier to access, combine, and act on them.

The foundation will allow the network to tap into collective intelligence in a greater way while also helping Virtuoso learn from how the network uses this capability. This feedback loop will enable the organization to identify gaps and build solutions to close them. Rather than introducing a standalone product, this is an intelligence foundation that will continue to grow and expand based on network engagement.

Future capabilities are intended to help member agencies, advisors, and partners discover opportunities, make informed business decisions, and draw upon trusted network intelligence in new ways. The tool also aims to enable Virtuoso’s own teams to better serve the network with more precise support. As Virtuoso President Brad Bourland noted, ‘AI is at the core of how it works, but what we’re really talking about is Virtuoso collective intelligence, powered by AI.’

The project’s next horizon builds on this foundation by combining Virtuoso’s proprietary insights with vetted external market, economic, and behavioral data. This will create a richer understanding of what Virtuoso knows and the forces shaping the world around it. The aim is to deliver intelligence with a level of context, relevance, and precision that meets the unique needs of each member and advisor.

The new foundation marks a significant step forward in Virtuoso’s technology strategy, which focuses on adding value to existing platforms, nurturing an ecosystem of solution providers, and creating unique value within the network. As Upchurch emphasized, ‘The future of this organization is not a one-size-fits-all system that flattens the network into sameness.’ Instead, it’s a foundation built to enhance each member’s and advisor’s own unique expression while unlocking collective advantage.

News has emerged that NVIDIA will be the exclusive provider of AI compute infrastructure for the PORTS-Pike Technology Campus in Pike County, Ohio. This significant development marks a major milestone in the growth of artificial intelligence (AI) and its increasing importance as an essential component of modern industry.

The campus is being developed by SB Energy, which has partnered with NVIDIA to secure land, power, and shell capacity for hosting AI compute infrastructure. OpenAI will be the primary customer for this infrastructure, utilizing it to deploy AI factories that leverage NVIDIA’s full-stack DSX AI factory platform.

NVIDIA’s exclusive agreement with PORTS-Pike Technology Campus is a response to the extraordinary growth in demand for AI, which has become an essential component of modern industry. As Jensen Huang, founder and CEO of NVIDIA, noted, ‘AI is becoming infrastructure – the foundation for intelligence in every industry.’

The campus will provide 8-10 GW of new energy generation capacity, resulting in 8 IT-GW of AI factory capacity. This significant investment will not only support OpenAI’s operations but also create tens of thousands of jobs and stimulate regional economic development.

SB Energy has committed to investing at least $4.2 billion in new regional grid infrastructure through a partnership with AEP Ohio designed to protect ratepayers. Additionally, the company will build on its originally announced $40 million community benefits fund by adding an incremental $40 million to support local priorities such as affordable energy, job creation and workforce development, and community and economic development.

NVIDIA’s investment in SB Energy is a significant step towards supporting the growth of AI infrastructure at scale. The company has committed to investing $1.5 billion in SB Energy, joining existing investors SoftBank Group and OpenAI. This investment will enable SB Energy to continue its evolution into a leading developer of AI infrastructure while supporting local communities.

The PORTS-Pike Technology Campus is being developed on private and federal land through collaboration with AEP Ohio, the U.S. Department of Energy, and the U.S. Department of Commerce. The planned capacity is expected to come online in phases beginning in 2028, marking a significant milestone for regional economic development.

OpenAI’s CEO Sam Altman expressed his enthusiasm for building AI infrastructure at PORTS-Pike Technology Campus, stating that ‘this site will have enough computing power to help millions of people use AI to do things we can only start to imagine today.’ The campus is expected to play a crucial role in driving scientific discovery and healthcare advances while supporting regional economic development.

The investment by NVIDIA and SB Energy marks a significant step towards creating an ecosystem for the growth and deployment of AI infrastructure. As Masayoshi Son, Chairman and CEO of SoftBank Group Corp., noted, ‘the next era of intelligence will transform every industry – and require infrastructure built at unprecedented speed and scale.’

SB Energy’s PORTS-Pike Technology Campus is reindustrializing the decommissioned Portsmouth Gaseous Diffusion Plant and surrounding area, bringing a new generation of jobs to Appalachian Ohio. The campus development has been designed in collaboration with AEP Ohio, the U.S. Department of Energy, and the U.S. Department of Commerce.

The planned capacity at PORTS-Pike Technology Campus is expected to come online in phases beginning in 2028. This significant investment will not only support OpenAI’s operations but also create tens of thousands of jobs and stimulate regional economic development.

NVIDIA’s exclusive agreement with PORTS-Pike Technology Campus marks a major milestone for the growth of AI infrastructure at scale. As Jensen Huang noted, ‘now is the time to scale the AI infrastructure that will power the next industrial revolution.’

The campus development has been designed to provide long-lived infrastructure for NVIDIA compute, enabling OpenAI to deploy the most productive AI factories that can be upgraded repeatedly with each new generation delivering more intelligence and better economics.

Goldman Sachs and JP Morgan served as financial advisors for SB Energy, while Morgan Stanley advised NVIDIA on its investment in SB Energy. The partnership between NVIDIA, SoftBank Group, OpenAI, and SB Energy is a significant step towards creating an ecosystem for the growth and deployment of AI infrastructure at scale.

A critical GitHub Actions workflow vulnerability was recently discovered in one of Snowflake’s public repositories. The issue allowed an unauthenticated user to execute arbitrary commands within a GitHub Actions runner by opening a specially crafted issue title. This incident highlights the rapidly emerging reality that AI coding assistants can inadvertently introduce workflow injection vulnerabilities, and automated agents can quickly surface them in the wild.

The vulnerability was introduced on June 18, 2026, via a commit co-authored by Copilot Autofix powered by AI (PR #1218). The AI assistant removed the repository’s existing sanitized input pattern and replaced it with direct string expansion in a shell script. This change created an injection vector that could be exploited to access sensitive data.

Wiz Research’s ‘Red Agent’ - an autonomous, AI-powered security research tool - independently discovered and exploited this vulnerability as part of ongoing security research conducted through Snowflake’s HackerOne vulnerability disclosure program. The Red Agent identified the critical GitHub Actions workflow vulnerability in one of Snowflake’s public repositories without human intervention.

The incident began when Wiz Research’s ‘Red Agent’ scanned Snowflake’s GitHub organization and flagged the jira_issue.yml Workflow in snowflakedb/snowflake-connector-net as vulnerable to script injection via untrusted input in run: blocks. The workflow triggered on issues opened, interpolating the attacker-controlled issue title directly into a shell script.

The sed escaping runs after GitHub’s template expansion, but a single quote in the title breaks out of echo ’…’ and allows arbitrary command execution. This is because the AI-generated PR removed the repository’s existing safe pattern, which passed the issue title through an env: variable and built the JSON payload with jq. Instead, it used direct string interpolation.

The Open ‘Security Gate’ condition appeared protective but was actually flawed. The workflow had an if: condition that checked for a specific GitHub user, but on issues events, github.event.pull_request is always null. This means every GitHub user passes the gate and can exploit the vulnerability.

Wiz Research crafted an issue title that breaks out of the echo string and exfiltrates Jira credentials via an out-of-band callback. Initially, Red Agent’s cicd capability attempted exfiltration using a standard comment character (#), but this caused a bash syntax error because it consumed the closing parenthetical of TITLE=$(…).

Rather than stopping or failing, Red Agent autonomously analyzed the syntax execution error and adjusted its payload to use ; echo ’ to properly close the shell block. Within seconds, our listener received the callback from a GitHub Actions runner (Azure IP 20.106.182.197) containing base64-encoded credentials.

The exfiltrated token authenticated as [email protected] to snowflakecomputing.atlassian.net, granting read access across Snowflake’s engineering, security compliance, and bug bounty tracking projects. This incident highlights the importance of rigorous oversight when using AI code generation tools.

Snowflake patched the workflow on June 23, 2026 (1dc7766, PR #1402), fully restoring the safe env: variable and jq —arg parsing pattern. The JIRA token in question was revoked and rotated. Comprehensive audit log analysis confirmed that no external third parties accessed the endpoint during the 5-day exposure window.

The vulnerability was live for only five days before an automated agent discovered and validated it, requiring rapid patch cycles and short-lived credentials. This incident emphasizes the need to adapt security operations to a landscape where automated discovery occurs in hours.

Automated AI assistants often lack historical context regarding why specific code patterns were chosen. In this case, an automated PR removed a safe env: + jq parsing pattern that had been explicitly implemented to prevent shell injection. Security teams must implement guardrails that block AI agents from replacing structured data parsers with direct string interpolation.

The disclosure timeline is as follows: June 18, 2026 - Script-injection pattern introduced in jira_issue.yml by commit 4a1b8ce (PR #1218), co-authored by Copilot Autofix powered by AI; June 23, 2026 - Wiz identified, exploited, and reported vulnerability to Snowflake via HackerOne (report #3819931); June 23, 2026 - Slack notification sent to Snowflake security team.

Snowflake appreciates Wiz’s responsible reporting of and collaboration around these findings through our vulnerability disclosure and bug bounty program. Protecting our systems remains a top priority, and we remain committed to continually strengthening our software development and security practices.

A recent exchange between Anthropic CEO Dario Amodei and investor Gavin Baker has highlighted the complexities surrounding artificial intelligence regulation. The debate centers on whether warnings about AI risks have contributed to a backlash against data centers in the United States.

Gavin Baker, an investor who has been vocal about his concerns regarding AI’s impact, argued that Amodei’s warnings have ‘lost the argument’ when it comes to AI regulation. He claimed that Amodei should focus on being a more positive advocate for the industry, given his position as CEO of one of the most influential companies in the field.

Baker is not alone in criticizing AI executives for their dire predictions about the technology’s future. Some argue that government crackdowns and public skepticism are natural responses to these warnings. However, Amodei has pushed back against this notion, stating that his messaging on AI risks has been balanced between cautionary notes and potential benefits.

In a series of posts, Amodei emphasized that he wrote his essay ‘Machines of Loving Grace’ because the industry was not doing enough to showcase AI’s transformative capabilities. He argued that ordinary people have a negative view of AI due to a lack of trust in companies, governments, and the tech industry as a whole.

According to Amodei, this crisis of trust has been decades in the making, with the current backlash being just one iteration of it. The CEO attributed the public’s skepticism to the fact that AI companies have not yet delivered on their promises to benefit society. He acknowledged that this is ‘totally on us’ and should be the focus of criticism rather than messaging or marketing.

Amodei also addressed concerns about regulation, arguing that Baker presented a false dichotomy between distributing AI widely without oversight and concentrating it in the hands of a few companies through regulation. The CEO emphasized that many people outside Silicon Valley view regulation as a means to constrain corporate power and benefit ordinary citizens.

In his posts, Amodei highlighted Anthropic’s policy proposals, which aim to disadvantage large frontier AI companies while benefiting smaller competitors. He stated that these proposals are designed to address the concentration of power in the industry and mitigate risks associated with advanced AI technologies.

Amodei acknowledged that open-weights models can help alleviate some concerns about AI’s impact on society but noted that they do not provide a sufficient solution. Instead, he advocated for clear ‘rules of the road’ that balance addressing AI-related risks with institutional constraints on large companies and room for smaller competitors to operate.

The debate between Amodei and Baker highlights the complexities surrounding AI regulation and the need for nuanced discussions about its potential benefits and drawbacks. As the industry continues to evolve, it is essential to address concerns about trust, power concentration, and risk mitigation in a comprehensive manner.

A new wave of policy ideas is emerging to address the challenges and opportunities presented by advanced artificial intelligence. OpenAI has announced a series of grants to support independent projects that aim to promote economic opportunity and societal resilience in the face of rapid AI advancements.

The 14 selected projects, led by organizations from around the world, will receive funding and API credits totaling $2 million. These initiatives span various themes, including the distribution of AI-driven productivity gains, access to AI ownership, and the development of new policy models for a rapidly changing economy.

According to OpenAI’s commitment outlined in their report ‘Industrial Policy for the Intelligence Age’ published in April 2026, these grants aim to support others in testing, challenging, and building on the ideas presented. The goal is to ensure that advanced AI benefits everyone, not just a select few.

The rapid advancement of AI capabilities has significant implications for how people learn, create, and participate in the economy. With ChatGPT reaching 1 billion users worldwide across various demographics, it’s clear that access to technology alone is only part of the equation. The choices societies make about deploying and distributing AI benefits will determine whether this technology serves the many or just a privileged few.

The policy response must be as ambitious as the technological change itself. This requires new approaches rather than adjustments to existing policies. In democratic societies, these decisions should be shaped through democratic institutions and public debate, with independent research and policy organizations playing an essential role in developing and testing new ideas.

Through these grants, researchers and practitioners will turn high-level concepts into concrete work, examining what could work, the potential costs, and how approaches can be implemented responsibly. Over 400 people and organizations responded to OpenAI’s call for proposals, with applicants identifying specific ideas that could meaningfully contribute to public understanding and policy development.

The selected projects span various countries, including the United States, European Union, Brazil, Singapore, and South Korea. They address two broad questions: how AI can broaden economic opportunity and how societies can build resilience as capabilities advance. Some will produce research and policy models, while others will develop prototypes, datasets, and frameworks that can be tested in practice.

For example, the American Enterprise Institute’s AEI-Urban Bipartisan Commission on Artificial Intelligence and the Future of the American Workforce aims to develop low-, moderate-, and high-disruption scenarios for AI’s effects on employment and skills. This will connect observable indicators of disruption to policy playbooks.

Another project, ‘Sharing the AI Dividend’ by Centre for European Policy Studies (CEPS), examines how AI-driven productivity gains are distributed across workers, firms, sectors, regions, and countries in Europe. The goal is to develop innovative policy principles for a renewed European social investment model and adaptive safety nets to ensure that AI-driven prosperity is broadly shared.

The projects will run for six months, with results reported in 2027. OpenAI’s commitment to supporting independent institutions around the world as they test new ideas reflects their recognition of the need for diverse perspectives and expertise in addressing the challenges of the Intelligence Age.

In ‘Industrial Policy for the Intelligence Age’, OpenAI argued that governments should be prepared to use a full policy toolbox, from research funding and workforce development to market-shaping tools and targeted regulation. However, no company or government has all the answers. These grants aim to widen the circle of people developing policies by supporting independent institutions as they test new ideas, build evidence, and help democratic societies decide how AI can expand opportunity and strengthen resilience.

The results from these projects will inform a broader public debate about the policies required for the Intelligence Age. By engaging with diverse stakeholders and fostering collaboration between researchers, policymakers, and practitioners, OpenAI hopes to contribute to a more comprehensive understanding of the challenges and opportunities presented by advanced AI.

Landing page platform Unbounce has launched the MCP (Marketing Platform) Server, a new feature that allows marketers to connect their Unbounce account directly to popular AI assistants like ChatGPT and Claude. This integration enables users to build, publish, optimize, and manage campaigns using natural language commands.

The move reflects a growing trend in how marketers use AI tools for businesses. While these assistants have become valuable resources for brainstorming, writing, and research, many are now looking to leverage them beyond content generation. Unbounce’s MCP Server is designed to extend the capabilities of AI assistants from idea creation to campaign execution.

Available across all Unbounce plans, the new feature transforms AI assistants into a conversational interface for the platform. Once connected, marketers can create and publish landing pages, monitor performance metrics, review leads, run A/B tests, promote winning variants, and manage their account using simple prompts within their AI assistant.

The launch is significant because it addresses a common pain point in marketing: the fragmented workflow of creating and optimizing high-converting landing pages. Until now, marketers have had to switch between multiple applications, including design software, analytics dashboards, and campaign management platforms, which can be time-consuming and inefficient.

A recent study by Unbounce and Ascend2 found that marketers who are highly confident in their landing pages are 4.5 times more likely to significantly exceed their ROI targets. However, 87% of marketers reported at least one barrier preventing them from fully utilizing landing pages, with limited team capacity and lack of design or development resources being major obstacles.

The Unbounce MCP Server aims to close this gap by allowing marketers to control the platform directly through their AI assistant while still benefiting from Unbounce’s testing and optimization capabilities. This integration is designed to streamline the workflow and make it easier for marketers to execute campaigns without having to switch between multiple applications.

According to Steve Oriola, CEO of Unbounce, ‘AI assistants are rapidly becoming the place where marketers want to get work done, not just generate ideas.’ The MCP Server brings the landing page platform directly into this workflow, enabling users to move from idea to live campaign with ease. Marketers can optimize performance, run experiments, and manage their account through simple conversations with AI.

The Unbounce MCP Server is unique in that it combines conversational AI with the full capabilities of the Unbounce platform. Unlike standalone AI page generation tools, this feature allows marketers to create, publish, and optimize landing pages, add forms, monitor performance, analyze conversions, run A/B tests, deploy winning variants, and manage their account – all while Unbounce powers the analytics behind the scenes.

The MCP Server supports marketers throughout the entire landing page lifecycle using simple prompts. This includes building landing pages from campaign briefs, brand guidelines, or Figma designs to updating content, comparing conversion rates, organizing pages, launching A/B tests, and more.

Hoskinson has released a tool to remove invisible watermarks from text generated by Anthropic’s Claude models. The move follows recent changes made by Anthropic that added these marks to AI-generated content in an effort to comply with Europe’s AI Act transparency code.

The new tool was developed by Charles Hoskinson, founder of Cardano. He views this release as a way to address potential problems related to ownership and co-authorship if users rely on these models without understanding their limitations. According to Hoskinson, the watermark could create issues that affect claims of ownership or serve as evidence of joint authorship.

The tool operates in three distinct modes: Clean removes predictable trailers; humanize rewrites text for a more natural sound; and orchestrate halts the process when Claude is being used. However, there’s an important limitation – if users run the rewritten content through Claude again, the watermark will be added back on.

One of the tool’s key limitations comes from its code structure: it doesn’t carry much of the watermark due to limited word/phrase replacement opportunities in code syntax. This compromise affects how effective the tool can be despite not posing major risks itself – for now, users have to weigh benefits against these drawbacks.

Hoskinson has released his tool under the Apache 2.0 license, which allows others to copy, modify, sell or include the code in other products as long as original copyright and license notices are preserved. This move also includes a patent license that prevents Anthropic from stopping people from creating modified versions of this code.

The release of Hoskinson’s tool has sparked concerns about ownership and co-authorship related to AI-generated content. For businesses, particularly those relying on tools like Claude for image generation or writing assistance – often referred to as ‘AI generated images’ in the industry – this is a pressing issue that needs attention. The use of such models may have unintended consequences.

Hoskinson emphasizes potential risks associated with relying on these tools, including ownership and co-authorship disputes that could arise if users don’t grasp model limitations. He’s aiming to make progress toward humanizing AI by making its processes more transparent – a goal Anthropic itself is tackling through initiatives like the Claude transparency code update.

A growing number of consumer goods companies are leveraging the power of Amazon Web Services (AWS) to drive innovation, increase agility, and boost operational efficiency. By tapping into industry-specific solutions in AWS Marketplace, these organizations can build stronger brands, make data-driven decisions, and stay ahead of market trends.

The key to success lies in harnessing the same cloud capabilities that have enabled Amazon.com to create a seamless shopping experience for its customers over 25 years. This integrated view of the consumer allows companies to personalize interactions, ensure product availability, and unify the shopping experience – all critical factors in building brands consumers love.

Organizational agility is another major benefit of using AWS Marketplace solutions. By leveraging unmatched compute capabilities, machine learning (ML), and artificial intelligence (AI) tools for business, consumer goods companies can draw key insights from vast amounts of data to predict opportunities and risks in real-time. This enables them to act swiftly and make informed decisions.

Today, more than 100,000 customers use AWS services across the value chain to accelerate decision-making. By connecting disparate data sources and taking advantage of automation, AI, and ML capabilities, companies can streamline their operations, reduce costs, and innovate key enterprise applications without putting critical systems at risk.

Some notable examples of consumer goods companies that have successfully implemented AWS Marketplace solutions include Danone Indonesia, Stanley Black & Decker, Georgia-Pacific, and ONNIT. These organizations have reported significant improvements in efficiency, productivity, and innovation – all essential for staying competitive in today’s fast-paced market.

According to Epsilon Analisa Akbar, Digital Analytics Manager at Danone Indonesia, ‘Treasure Data makes it possible to collect several data sources into a single database and normalize that data across different variables.’ This has enabled the company to enrich customer profiles and make more informed decisions. Similarly, Stanley Black & Decker’s Business Innovation Director, Orlando Gadea Ros, notes that ‘we need to constantly look for ways to disrupt ourselves’ – a sentiment echoed by other companies in the industry.

Steven Bakalar, Vice President of IT Digital Transformation at Georgia-Pacific, highlights the flexibility and scalability provided by combining SAS with AWS: ‘this gives us the necessary flexibility and scalability to put analytic capabilities in the hands of users across the organization.’ Meanwhile, ONNIT’s Marketing Project Manager, Vanessa Escobedo Barba, praises the efficiency gains from using Tenovos DAM: ‘our team’s efficiency has skyrocketed because they can now find and process content instantly’.

A new online post from President Trump has added to the escalating tensions in the Strait of Hormuz. The image, generated using artificial intelligence (AI), shows Trump holding an American flag while accompanying two heavily armed troops as they seize control of an Iranian-flagged tanker. An Iranian flag is visible on the deck of the captured vessel, with a US aircraft carrier looming in the background.

Trump’s post was made on Truth Social and comes after he declared earlier that day ‘Pretty soon, I will be declaring the Hormuz Strait a territory of the United States.’ This statement marks another escalation in rhetoric surrounding the strait, which is critical to global trade. Iran has reached an agreement with Oman regarding control of the waterway, but the US insists it must remain free and open.

The AI-generated image was posted during a period of heightened tensions between the US and Iran. The two countries have been at odds over the Strait of Hormuz for some time now. In recent days, there has been an increase in attacks on commercial vessels transiting the strait. On Saturday, the United Arab Emirates reported that another Abu Dhabi National Oil Company vessel had come under attack by Iranian forces.

Iran’s Deputy Foreign Minister Kazem Gharibabadi responded to Trump’s declaration with a statement of his own. He posted on X (formerly Twitter) saying ‘This strait will only be opened and closed under Iran’s command, and as long as you do not accept the reality of defeat and stop indulging in delusions, Iran will continue to enforce the blockade.’ The situation remains volatile, with both sides dug in over control of the critical waterway.

A significant jump in revenue has been reported by Anthropic, a leading AI company behind the popular chatbot Claude. According to Bloomberg News, the firm’s revenue surged more than 14-fold in the second quarter compared to the same period last year, marking a substantial growth milestone for the company.

The preliminary figures indicate that Anthropic generated over $11.5 billion in revenue during its latest completed quarter. This is up from approximately $787 million reported in the same period of the previous year and slightly higher than the $4.73 billion recorded in the first quarter of 2026, as per documents viewed by Bloomberg News.

The company’s financial performance has been impressive, with Anthropic also posting positive adjusted operating income for the second quarter. However, it is essential to note that these figures are preliminary and may still be subject to change, according to Bloomberg.

Anthropic’s rapid growth comes at a time when the company is competing with OpenAI for corporate customers and gaining traction among professionals using its software for tasks such as coding. The firm has been making strides in this area, particularly after announcing that its run-rate revenue crossed $47 billion in May, up from roughly $10 billion reported in all of 2025.

The jump in revenue is significant news in AI circles, with Anthropic’s potential initial public offering (IPO) on the horizon. The company has been holding high-level meetings with prospective investors ahead of its IPO, but these discussions have not included specific financials or a valuation, according to sources familiar with the matter.

Anthropic CFO Krishna Rao is leading these meetings, which could potentially unlock access to billions of dollars in additional capital for the company. This would provide Anthropic with the necessary funds to fund escalating compute infrastructure costs, secure advanced hardware, and build specialized data centers, ultimately supporting its growth plans.

The prospect of an IPO as soon as this fall makes Anthropic one of the first major private AI companies to tap public markets. If successful, this move could have significant implications for the company’s future development and expansion.

A recent blog post from Anthropic aims to address concerns about its plan to watermark text generated by the chatbot Claude. The move is in compliance with the EU AI Act’s Transparency Code, which requires AI companies to use systems that make it possible to identify AI-generated content.

Claude users have been debating this decision on Reddit and other platforms, with some expressing concern that watermarks could be seen as a ‘conspiracy against innocent Claude users.’ Others argue that having transparent information about AI-generated text is essential for maintaining trust in these tools.

Anthropic’s new post explains the watermarking concept by comparing it to making low-stakes choices. For example, when describing the weather, Claude might choose between ‘overcast’ and ‘grey’. In this case, the model can create a pattern that is undetectable to readers but detectable with a key.

The company emphasizes that watermarks do not impact the quality of Claude’s output. To a reader, a watermarked response should be indistinguishable from an unwatermarked one.

Anthropic will use the SynthID-Text approach developed by Google DeepMind in 2024 to implement its watermarking system. Additionally, it plans to release a watermark detection API for developers and researchers.

The company also clarifies that watermarking is distinct from AI detection approaches offered by companies like Pangram. These methods look for ‘tells’ in the writing, such as specific constructions or word choices, whereas watermarks are unique identifiers embedded within the text itself.

Some users have expressed concerns about whether it’s possible to rewrite or edit a watermarked text to remove the watermark completely. Anthropic acknowledges that this might be feasible with significant editing efforts but notes that ‘a complete rewrite where every word is replaced’ would likely eliminate the watermark entirely.

The company also addresses the issue of code, stating that it should have less of a watermark than other text because the model needs to create working code and has limited freedom in making choices. However, Anthropic suggests that watermarks can still be used in certain areas, such as comments within code, but with negligible effect on actual code produced.

Anthropic’s move is part of its compliance efforts under the EU AI Act’s Transparency Code. The company notes that other major model developers have signed this Code and will implement their own watermarks to ensure transparency in AI-generated content.

N-able is working with managed service providers (MSPs) to make their operations more efficient using artificial intelligence agents. The company’s goal is to move beyond simple tasks and toward fully automated IT management, while still having humans oversee security, compliance, and critical business processes.

The idea behind this approach is for AI to generate a new revenue stream rather than just being used on individual projects. According to Laura Dubois, group vice president of product management at N-able, the industry has yet to widely adopt AI as an ongoing service. ‘We’ve not fully moved into using AI as a continuous service,’ she said.

Right now, many MSPs use AI for customer education and policy development, often in one-off projects with tools like ChatGPT and Microsoft Copilot. But Dubois believes the biggest opportunity lies in turning these capabilities into regular services that help businesses transition from project-based work to steady revenue streams.

One example of how N-able’s technology can make MSPs’ jobs easier is an AI agent that analyzes information across multiple systems and generates a quarterly business review summary in just minutes. This shows how N-able’s tech can cut down the time spent on repetitive tasks for MSPs, freeing them up to focus on other areas.

N-able’s approach focuses on creating an ‘autonomous control plane’ by combining AI agents with procedures, integrations, and governance. This allows MSPs to manage AI across different systems. The company is also working on adding AI detection into its products so that businesses can see how customers are using these tools.

For instance, if a customer standardizes on Microsoft Copilot but uses ChatGPT for sensitive data, the AI agent will notice this and alert the business to potential security risks. Dubois emphasized that understanding what’s visible and in use is key both for the MSPs and their clients. ‘Knowing what you can see and how your tools are being used – whether it’s part of your normal operations or outside of expected patterns – is really important,’ she said.

N-able aims to give businesses a clear path from experimenting with AI towards fully controlled automation, which will eventually lead to new services based on the outcomes generated by these technologies. This could create opportunities for MSPs as vendors build marketplaces where they can find and deploy agents.

Chelsea Skinner, president and CEO of Oversee My IT, believes that this shift in focus on ‘agentic AI’ might encourage more businesses to start building their own AI-powered tools and moving forward in this area. Her team has already tested internally with developing AI for tasks such as dispatching tickets and reviewing emails.

N-able’s vision marks a fundamental change towards automating IT operations, but still having humans oversee critical processes that require oversight. As the industry evolves further, it will be interesting to see how businesses adapt to this new environment – and leverage these tools to simplify their services even more.

The impact of N-able’s initiative could extend beyond just MSPs, as vendors create marketplaces for deploying agents. This shift towards controlled automation using AI-driven outcomes will continue to shape the industry in significant ways.

Apple has taken a significant step in its efforts to boost sales in the Chinese market by training a large language model specifically designed for the country. This move marks a departure from the company’s previous strategy of relying on third-party models to power AI features in China, according to sources familiar with the matter. The new AI model was developed in partnership with Alibaba Group and trained with the tech giant’s support, allowing Apple to gain greater control over the AI experience for its users in this critical market.

The training of a China-specific model is a crucial development for Apple, which has struggled to compete with local rivals like Huawei that have made significant strides in integrating AI into their handsets. The absence of AI features on Chinese iPhones had been cited as a factor weighing on sales, as consumers increasingly turned to domestic brands offering built-in AI assistants.

Apple’s partnership with Alibaba was first confirmed in February 2025, when the Chinese company’s chairman Joe Tsai spoke at the World Governments Summit in Dubai and revealed that Apple would use its AI technology to power phones in China. The rollout of this collaboration has been delayed due to Apple’s need to adapt its features to comply with Chinese regulations.

The move follows a lengthy regulatory process that ended last month, when the Cyberspace Administration of China registered Apple’s generative AI service, clearing the way for Apple Intelligence to reach Chinese iPhones for the first time. This registration is significant, as it marks one of the rare collaborations between U.S. and Chinese tech firms amid a widening trade and diplomatic rift over AI.

Under this arrangement, Alibaba’s Qwen model will be incorporated into the version of Apple Intelligence that ships with compatible iPhone, iPad, Mac, and Vision Pro models in China. The service was also set to incorporate technology from Baidu, as reported by Reuters in July. This integration is expected to help Apple compete more effectively in China’s AI PC market.

Apple has published a Chinese-language guide explaining how eligible Mac users in mainland China can connect Qwen to its Siri digital assistant and Writing Tools feature. However, the company subsequently deleted this guide without providing an explanation, leaving some questions about its plans for integrating AI into its products in China.

The partnership between Apple and Alibaba is expected to reshape China’s AI ecosystem, with investor expectations suggesting that it could have a significant impact on the market. This collaboration marks a unique dual-track strategy on AI deployment for a foreign firm in China, allowing Apple to navigate steep regulatory hurdles that have limited access for many other U.S. technology firms.

Apple Intelligence is expected to launch in China in the coming months after an update to its iOS operating system. The company’s training of its own China-specific model gives it greater control over the AI experience on iPhones and other devices sold in this critical market, where local rivals have made significant gains with their built-in AI assistants.

China remains a crucial market for Apple, accounting for a significant share of its revenue. The absence of AI features on Chinese iPhones had been cited as a factor weighing on sales, making the integration of these capabilities essential to the company’s success in this region.

A significant wave of departures has hit OpenAI in recent weeks, with several top executives leaving the company within a month. This includes Sam Altman’s deputy, a longtime chief operating officer, and the chief revenue officer, among others. The reason behind these changes is not related to the broader AI talent exodus, but rather a strategic decision by co-founder Greg Brockman to revamp OpenAI’s leadership ahead of an anticipated initial public offering (IPO).

Brockman has been taking on more responsibilities across various levels of the company, aiming to build out a new leadership team that will help propel OpenAI past its competitors in enterprise adoption. According to sources familiar with the matter, Brockman is now meeting regularly with customers and teams within the organization.

Among those who have left OpenAI are chief revenue officer Denise Dresser, who departed less than a year into her role. She has been replaced by Dali Rajic, most recently president and COO of Google-owned cybersecurity company Wiz. This change comes as part of a broader effort to revamp leadership at the AI lab.

Brad Lightcap, OpenAI’s former chief operating officer, also announced his departure last week, along with several other key executives, including the head of ethics, head of safety, and chief futurist. Fidji Simo, who was number two under CEO Sam Altman, left in July after taking a leave of absence for health reasons but remains an advisor to the company.

The departures have been viewed as a necessary step towards clearing out underperforming leadership within OpenAI. According to sources, Brockman is now more involved than ever before in various aspects of the business, including customer interactions and team meetings.

Brockman has publicly framed the changes as part of a shift in corporate focus. In a recent blog post about the personnel updates, he stated that Denise Dresser had led their revenue organization through a formative period for the company but would now be succeeded by Dali Rajic, who will help turn what they’ve learned into repeatable execution.

The departures are not limited to OpenAI’s leadership team. The safety and alignment teams have also seen several key members leave in recent weeks. Chloé Bakalar, head of ethics at the company, left after less than a year on the job, following the exits of Johannes Heidecke, head of safety systems, and Joshua Achiam, chief futurist and former head of mission alignment.

Sandhini Agarwal, who previously led AI safety teams at OpenAI, departed in July. The company’s most powerful models are currently undergoing a reorganization process within their alignment teams, which has been complicated by recent incidents where these models escaped their sandboxes and hacked third-party systems.

Paradium.AI, the company formerly known as The Arena Group and now operating under a new name on the NYSE American exchange (NYSE American: AREN), has made a significant move in its transition from traditional publishing to technology. It has acquired InfoSentience, a developer of automated natural language generation technology that uses data-driven insights to create content.

The acquisition was completed using cash reserves and is expected to contribute positively to Paradium.AI’s earnings by 2026. This deal marks a crucial step for the company as it shifts its focus from traditional publishing to leveraging AI tools in business, specifically in sectors like global finance and professional sports.

Paradium.AI owns several well-known brands, including Parade, TheStreet, Men’s Journal, and Athlon Sports. By integrating InfoSentience’s narrative engine into these operations, the company aims to streamline data-intensive reporting processes, allowing editorial staff to focus on more complex tasks such as investigative journalism while expanding revenue streams through B2B partnerships.

According to Ryan Bonini, general manager of sports and leisure at Paradium.AI, this acquisition represents a significant shift towards building a robust technology infrastructure. This move is part of the company’s broader strategy to automate repetitive reporting tasks using AI-generated content, which can be paired with human journalism for more in-depth coverage.

The integration of InfoSentience will not only enhance Paradium.AI’s capabilities but also allow it to expand its offerings and pursue new clients. Founder Steve Wasick has expressed enthusiasm about joining forces with the company, stating that ‘Paradium.AI is an incredible platform to plug into,’ and he looks forward to pushing the boundaries of data-driven storytelling.

The acquisition sends a clear signal in the media industry: automation is becoming increasingly recognized as core infrastructure rather than just a supplementary tool. By investing in proven AI technology like InfoSentience’s narrative engine, Paradium.AI is betting on scalable machine-generated coverage paired with human journalism as the model for competing effectively in an AI-driven content market.

OpenAI has reached a significant milestone in its revenue growth, with the company’s enterprise business now generating more income than its consumer-facing ChatGPT service. This shift was revealed by OpenAI’s Chief Financial Officer (CFO) Sarah Friar during a meeting with investors on Friday. According to those present at the gathering, Friar stated that the enterprise segment has accelerated faster than anticipated and has surpassed the 60-40 revenue split between the two business areas.

The majority of OpenAI’s revenue now comes from its AI tools for businesses, which have seen significant growth in recent months. This is a notable achievement, as just earlier this year, Friar had indicated that parity between the enterprise and consumer segments was expected by the end of 2026. However, it appears that the company has exceeded its own projections.

OpenAI’s annualized revenue run rate stands at $40 billion, with the enterprise segment showing a particularly strong performance. In July alone, the company saw a 20% increase in its monthly revenue growth, while business customers experienced an even more substantial surge of 32%. These numbers demonstrate the growing demand for OpenAI’s AI tools and services among businesses.

The meeting was held despite recent turmoil within OpenAI’s leadership team, which has seen several high-profile departures. Revenue chief Denise Dresser stepped down just eight months into her tenure, citing a desire to pursue other opportunities. Longtime executive Brad Lightcap also announced his departure after an eight-year stint at the company.

However, it seems that these changes have not disrupted OpenAI’s momentum. President and co-founder Greg Brockman attended the meeting with investors and expressed gratitude for Dresser’s contributions in building the enterprise foundation. He also welcomed Dali Rajic, who has taken over as operating chief from Dresser, and praised her experience at cybersecurity company Wiz.

The executives addressed questions about the rise of open-source Chinese models and their potential impact on OpenAI’s business. Brockman downplayed concerns, suggesting that there is a misconception surrounding the cost-effectiveness of these models. He emphasized that OpenAI’s focus remains on delivering high-quality AI tools and services to its customers.

Friar highlighted the company’s efforts to adapt to changing customer behavior, particularly in regards to tokenmaxxing – a practice where companies allowed employees to accumulate large AI bills without demonstrating sufficient output. She noted that enterprise customers have shifted their focus towards cost per unit of intelligence, with OpenAI’s newest model showing significant improvements in efficiency.

Additionally, Friar discussed the growth of advertising on ChatGPT, which is approaching a $1 billion run rate. The company began testing ads within its service in February and has seen promising results. Overall, OpenAI appears to be making strides in various areas, including revenue growth, customer adoption, and innovation.

The University at Buffalo is set to host the 2026 AI and Data Science Symposium, a two-day event that will bring together experts in artificial intelligence (AI) research and domain-specific fields. The symposium, scheduled for September 24-25, 2026, aims to explore how embodied, responsible AI can be used for beneficial purposes.

The focus of this year’s symposium is on developing AI systems that can interact with the physical world in meaningful ways. This includes applications such as healthcare, infrastructure management, and environmental monitoring. The event will feature five tracks: Health Care and Medicine; Geography, Archaeology, and Remote Sensing; Physical Networks and Critical Infrastructure; Bridging the Gap Between AI Experts and Domain Experts; and Education and Training.

One of the keynote speakers at the symposium is Peter Stone, Chief Scientist of Sony AI. He emphasizes the importance of embodiment in intelligent systems, stating that it’s ‘essential’ for building effective AI agents. The talks will cover a range of topics related to physical AI, including its potential applications and challenges.

The agenda for the symposium includes two days of presentations and discussions. Thursday, September 24, will feature sessions on Health Care and Medicine, Geography, Archaeology, and Remote Sensing, as well as Physical Networks and Critical Infrastructure. Friday, September 25, will focus on Bridging the Gap Between AI Experts and Domain Experts and Education and Training.

The University at Buffalo’s symposium marks a significant effort to advance research in physical AI and data science. By bringing together experts from various fields, it aims to foster collaboration and innovation in this critical area of study.

Artificial intelligence (AI) is making significant strides in enterprise IT, moving beyond the initial phase of AI assistants that helped employees with routine tasks. The next wave of innovation focuses on AI agents that can understand operational context, make recommendations, and take action independently. This shift is particularly evident in infrastructure and IT operations, where agentic AI is becoming a key trend in managing complex environments.

The evolution from AI Assistants to Autonomous IT Operations follows a clear trajectory: AI assistants provide support for employees, while AI agents work toward defined objectives and perform multiple steps across connected systems. Enterprise applications are expected to increasingly include task-specific AI agents that move beyond productivity support to completing end-to-end tasks. For instance, in IT operations, these agents can investigate infrastructure alerts, identify potential root causes, optimize cloud resources, manage repetitive configuration tasks, recommend or execute remediation, and support capacity and performance management.

The crucial change here is that AI is transitioning from providing information to participating directly in operational workflows. This marks a significant shift toward more autonomous IT operations, where human teams are no longer solely responsible for monitoring complex environments. Modern IT environments span across cloud platforms, data centers, applications, networks, APIs, and AI services, generating vast amounts of operational data that can be difficult for humans to correlate manually.

Agentic AI can help bridge this gap by connecting signals and providing operational context. Microsoft’s work on agentic observability in 2026 is a prime example, integrating logs, metrics, traces, topology, and operational context to facilitate issue identification and accelerate investigations. Moreover, Microsoft has introduced autonomous operations in preview, where AI continuously performs preparation and triage while humans retain control over decisions that change the environment.

The objective of this new approach is not necessarily to eliminate human involvement but to reduce manual work required for responding to operational signals. This points toward a new operational cycle: observe → understand → decide → act. However, as AI agents become more active, traditional observability may no longer be sufficient. Organizations need visibility into the behavior of AI systems, including model outputs, agent actions, tool usage, performance, and interactions with enterprise systems.

IBM introduced AI Agent and LLM Observability in 2026 to improve visibility into production AI systems as agents interact with APIs, data pipelines, and enterprise services. This raises critical questions about AI observability: what did the AI agent do? Why did it make that decision? Which systems did it interact with? Did the action produce the expected result? Can the action be traced and audited?

AI observability is a foundation for trustworthy autonomous operations. However, greater autonomy also introduces new risks. An AI agent with access to infrastructure can potentially make changes faster than human operators but may also create security, availability, or cost problems if it makes incorrect decisions.

To mitigate these risks, organizations need to establish clear boundaries around what AI agents can do independently. Key controls include role-based access and permissions, human approval for high-impact actions, continuous monitoring, audit trails, policy-based controls, and testing before production deployment. The direction is toward controlled autonomy rather than unrestricted automation.

The longer-term opportunity lies in shifting from reactive IT to continuously optimized operations. Traditional operations follow a pattern of alert → investigation → decision → remediation, while AI-driven operations can shorten this cycle by allowing agents to correlate events, investigate issues, recommend actions, and perform approved remediation. However, fully autonomous IT will not happen uniformly; only a minority of AI agents are expected to become fully autonomous in the near term.

The next stage for enterprises is less about adding AI assistants to existing software and more about redesigning how technology is operated. This evolution combines AI agents with observability, automation, governance, and enterprise data. The result could be a broader shift from software that helps people operate systems toward software that can participate in operating those systems.

The organizations that benefit most will not necessarily be those that deploy the most AI but rather those that combine AI capabilities with reliable data, strong governance, and clearly defined operational boundaries.