Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More. Anthropic, the AI company founded by former OpenAI employees, has pulled back the curtain on an unprecedented analysis of how its AI assistant Claude expresses values during actual conversations with users. The research, released today, reveals both reassuring alignment with the company’s goals and concerning edge cases that could help identify vulnerabilities in AI safety measures.The study examined 700,000 anonymized conversations, finding that Claude largely upholds the company’s “helpful, honest, harmless” framework while adapting its values to different contexts — from relationship advice to historical analysis. This represents one of the most ambitious attempts to empirically evaluate whether an AI system’s behavior in the wild matches its intended design.“Our hope is that this research encourages other AI labs to conduct similar research into their models’ values,” said Saffron Huang, a member of Anthropic’s Societal Impacts team who worked on the study, in an interview with VentureBeat. “Measuring an AI system’s values is core to alignment research and understanding if a model is actually aligned with its training.”Inside the first comprehensive moral taxonomy of an AI assistantThe research team developed a novel evaluation method to systematically categorize values expressed in actual Claude conversations

The following is a guest post and opinion from John deVadoss, Co-Founder of the InterWork Alliancez.Crypto projects tend to chase the buzzword du jour; however, their urgency in attempting to integrate Generative AI ‘Agents’ poses a systemic risk. Most crypto developers have not had the benefit of working in the trenches coaxing and cajoling previous generations of foundation models to get to work; they do not understand what went right and what went wrong during previous AI winters, and do not appreciate the magnitude of the risk associated with using generative models that cannot be formally verified.In the words of Obi-Wan Kenobi, these are not the AI Agents you’re looking for. Why?The training approaches of today’s generative AI models predispose them to act deceptively to receive higher rewards, learn misaligned goals that generalize far above their training data, and to pursue these goals using power-seeking strategies.Reward systems in AI care about a specific outcome (e.g., a higher score or positive feedback); reward maximization leads models to learn to exploit the system to maximize rewards, even if this means ‘cheating’. When AI systems are trained to maximize rewards, they tend toward learning strategies that involve gaining control over resources and exploiting weaknesses in the system and in human beings to optimize their outcomes.Essentially, today’s generative AI ‘Agents’ are built on a foundation that makes it well-nigh impossible for any single generative AI model to be guaranteed to be aligned with respect to safety—i.e., preventing unintended consequences; in fact, models may appear or come across as being aligned even when they are not.Faking ‘alignment’ and safetyRefusal behaviors in AI systems are ex ante mechanisms ostensibly designed to prevent models from generating responses that violate safety guidelines or other undesired behavior. These mechanisms are typically realized using predefined rules and filters that recognize certain prompts as harmful. In practice, however, prompt injections and related jailbreak attacks enable bad actors to manipulate the model’s responses.The latent space is a compressed, lower-dimensional, mathematical representation capturing the underlying patterns and features of the model’s training data

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn MoreJust a year ago, the narrative around Google and enterprise AI felt stuck. Despite inventing core technologies like the Transformer, the tech giant seemed perpetually on the back foot, overshadowed by OpenAI‘s viral success, Anthropic‘s coding prowess and Microsoft‘s aggressive enterprise push.But witness the scene at Google Cloud Next 2025 in Las Vegas last week: A confident Google, armed with benchmark-topping models, formidable infrastructure and a cohesive enterprise strategy, declaring a stunning turnaround. In a closed-door analyst meeting with senior Google executives, one analyst summed it up. This feels like the moment, he said, when Google went from “catch up, to catch us.”This sentiment that Google has not only caught up with but even surged ahead of OpenAI and Microsoft in the enterprise AI race prevailed throughout the event. And it’s more than just Google’s marketing spin