Learning Brief — 2026-07-24

What we covered

• AI news: Anthropic Launches Claude Opus 5: A Game Changer for AI Applications
• PM news: Hertility's AI Breakthrough in Women's Health: A Case Study for PMs
• PM learning: Building Trustworthy AI in Women's Health

Mental model

Build trust through transparency and user education in your AI products.

Summary

Anthropic has launched Claude Opus 5, a new AI model that offers nearly the same intelligence as its predecessor, Claude Fable 5, but at half the cost. This release marks a significant shift in the AI landscape, focusing on affordability and accessibility for various applications. Cognition has acquired Poke, a unique AI assistant that interacts conversationally, in a deal valued in the low nine figures. This acquisition highlights the trend of enhancing AI interactions to create a competitive edge in user engagement and satisfaction. Anthropic's Opus 5 model is designed to be more economical for coding, agents, and enterprise workflows, making it a practical choice for companies looking to innovate without breaking the bank. This model is available immediately, positioning it as a viable option for developers and businesses alike.

Recently, Hertility launched a groundbreaking AI tool aimed at revolutionizing women's health diagnostics by combining Bayesian diagnosis with scan automation. This is particularly significant because women’s health has long been an under-researched area, and the introduction of such technology can be a game-changer. What makes this launch intriguing for product managers is the strategic approach they took in building trust with end users. The team prioritized user feedback throughout the development process, ensuring that the technology not only met clinical standards but also addressed real concerns from potential users regarding privacy and accuracy. One key takeaway here is the importance of involving your target audience early in the product development process. Rather than just testing the product post-launch, they engaged in continuous dialogue with healthcare professionals and patients. This led to a product that not only solves a problem but does so in a way that users feel safe and confident in. Another lesson is the effective use of Bayesian methods, which allowed the team to enhance diagnostic accuracy while managing uncertainty—a crucial aspect in healthcare product management. For PMs aspiring to lead products that matter, this case illustrates the need for a rigorous yet empathetic approach to product development. In a world increasingly driven by technology, understanding user needs and building trust can be the differentiator that drives adoption and success.

Here’s the thing: when it comes to building AI products, especially in sensitive areas like women’s health, trust is your most valuable currency. Tulsi Patel from Hertility shared some powerful insights about marrying Bayesian diagnosis with scan automation to create a trustworthy AI diagnostic tool. What that means in practice is that your AI needs to be not just functional, but also credible. In a field historically overlooked, your users must believe in the accuracy and reliability of your product. The move here is to think beyond just the tech; consider the emotional landscape surrounding your users. For instance, think about how patients often feel vulnerable and uncertain when it comes to health diagnostics. Hertility's approach was to focus on transparency and user education alongside powerful AI. They didn’t just want to create a tool that gave results; they wanted to build a narrative that users could trust. One concrete example Tulsi provided was how they incorporated feedback loops where users could see how the AI arrived at its conclusions. This not only increases user confidence but also helps you refine the model based on real-world use. Connecting this back to your role as a Senior PM, it’s crucial to prioritize empathy alongside technical execution. This week, I challenge you to think about your current or upcoming product. How can you incorporate elements that enhance user trust? Maybe it’s through transparency in your data, user education initiatives, or even building feedback mechanisms that include user insights in your iterations. Remember, as you push for innovation, trust will be what helps you succeed in the long run.