Anthropic projects $70B in revenue by 2028: Report

The introduction of the transformer architecture in 2017 revolutionized artificial intelligence, becoming a foundation for major language models like OpenAI's GPT and Google's Gemini. The new Qwen3 variant, Brumby-14B-Base, utilizes a Power Retention technique, suggesting that attention may not be the only key to success in AI.

Anthropic is making waves in the tech industry with projections of revenues soaring to $70 billion by 2028, a significant leap from around $5 billion in 2025. This growth is not just impressive on paper; it signals a robust demand for AI technologies and positions Anthropic as a key player in the market. The company also anticipates becoming cash flow positive as early as 2027, which could attract more investors and boost innovation in the AI sector.

Anthropic and Iceland have launched a groundbreaking national AI education pilot that will provide teachers across the country, from Reykjavik to remote areas, with access to Claude, an advanced AI tool. This initiative is significant as it aims to enhance educational resources and empower educators, ensuring that students in all regions benefit from cutting-edge technology in their learning environments.

The latest version of the LLM API Adapter, which unifies OpenAI, Anthropic, and Google APIs, has just been released, and it's making waves in the tech community. This SDK-free update allows for direct communication with the APIs, enhancing usability and performance. With a solid 7-star rating on GitHub and positive feedback from users, this tool is set to streamline workflows for developers working with large language models. It's exciting to see how this innovation will simplify access to powerful AI technologies.

Recent research explores whether Large Language Models (LLMs) like those from OpenAI, Anthropic, and Google can develop self-awareness as they become more advanced. The study introduces the AI Self-Awareness Index (AISAI), a framework that uses game theory to measure this potential self-awareness through strategic differentiation in a game called 'Guess 2/3 of Average.' By testing 28 models over 4,200 trials against both humans and other AI, the findings could reshape our understanding of AI capabilities and their implications for future interactions.

Anthropic has secured a significant partnership with Cognizant, which will see the AI model Claude deployed to 350,000 of its employees. This collaboration not only enhances Cognizant's offerings but also positions Claude as a key player in enterprise AI solutions. The deal is a win-win, as it allows Cognizant to co-sell Claude models to its business clients, potentially transforming how companies leverage AI in their operations. This move highlights the growing importance of AI in the corporate landscape and could set a precedent for future collaborations in the tech industry.

A recent study by Anthropic reveals that while some large language models (LLMs) exhibit a degree of 'self-awareness,' they generally struggle with introspection, leading to highly unreliable descriptions of their internal processes. This finding is significant as it highlights the limitations of AI in understanding its own operations, raising concerns about the trustworthiness of AI-generated information.

Getting started with Claude Code is a breeze! This AI-powered coding assistant is designed to enhance your coding experience, making it feel like you have a smart colleague right in your terminal. With just a few simple steps, you can set up Claude Code and dive into your coding projects, whether you're a beginner or an experienced developer. This tool not only streamlines your workflow but also helps you tackle coding challenges more efficiently, making it a valuable addition to your programming toolkit.

Experts are expressing concern over a viral AI-generated image of Hurricane Melissa, which depicts birds that appear larger than football fields. This alarming portrayal has sparked discussions about its implications for meteorology and public perception.

The article explores the potential of AI personas in detecting human deception. It raises questions about the reliability of such technology and whether we should place our trust in AI's ability to identify lies.

As AI agents transition from prototypes to production, organizations are focusing on ensuring their accuracy and quality. Building custom LLM judges is a key step in this process, helping to enhance the reliability of AI systems.

Many teams are overcoming challenges in transitioning GenAI projects from pilot to production with the help of custom judges. This innovative approach is helping to streamline processes and enhance efficiency, making it easier for organizations to implement their AI initiatives successfully.

Moody's Risk Data Suite, integrated with the Databricks Data Intelligence Platform, offers financial executives innovative solutions to tackle modern risk and compliance challenges. This collaboration enhances data accessibility and analytics, empowering organizations to make informed decisions and navigate the complexities of today's financial landscape.

Databricks' latest research highlights that the challenge in deploying AI isn't just technical; it's about how we define and measure quality. AI judges, which score outputs from other AI systems, are becoming crucial in this process. The Judge Builder framework by Databricks is leading the way in creating these judges, emphasizing the importance of human factors in AI evaluation.