The discussion kicks off with a pressing question: a year ago, AI threatened to make Product Managers (PMs) obsolete, but now it seems everyone needs to become one. Ami and Mike from Anthropic offer insights into how the PM role is evolving.
Ami emphasizes that the core job of a PM remains unchanged: to bridge real-world human problems with technological solutions. What *has* changed is the pace of technological advancement, now shifting every two months instead of every five or ten years. This rapid change demands constant adaptability, challenging PMs to "throw away most of what I know about what used to work" and approach problems with fresh eyes. She likens the current state to the early days of product management when the role was less defined, requiring PMs to be general problem solvers who learn as they go.
Mike shares a recent "realization moment" from his individual contributor (IC) role at Anthropic. Despite having advanced AI like Claude, a project nearing shipment desperately needed a PM. He initially questioned the need, thinking Claude could handle it, but the PM's arrival highlighted the critical "glue" work: bringing stakeholders along, preparing customer success teams, looping in safeguards, and ensuring operational excellence. Mike concludes that this "convener" role, ensuring everything connects and the end-user needs are met, is more essential than ever due to the increased speed enabled by AI. He notes that while AI can provide insights, it doesn't yet possess the organizational pull or scheduling ability to orchestrate human collaboration effectively.
The conversation then delves into specific skills that are becoming obsolete and those gaining importance. Ami reflects on spending decades honing skills in detailed UI/UX design (e.g., "where should we put this button?"). Now, she finds it's faster and more effective to "build three versions and try them out." This personal "innovator's dilemma" highlights the need to shed old competencies. New critical skills include a higher tolerance for change, adaptability, strong judgment, and relentlessness in the face of ambiguity. Ami encourages "framing the chaos" to make it feel safe and plausible to engage with, fostering a culture where constant adaptation is the norm. Mike adds that clear leadership and "DRIs" (Directly Responsible Individuals) are crucial to navigate this chaos, providing a steady hand amidst rapid shifts.
Regarding *what* to build, Mike discusses the shift towards "agent-native" software. Initially, AI was seen as a sidebar feature, then integrated into existing features. The next stage is building products where agents can perform any action a human can. He notes that Anthropic uses Claude internally to create and iterate on UI for project tracking, demonstrating how interfaces can become "malleable" by agents. This raises questions about data provenance and how to make software reactive to organizational changes while maintaining user-friendliness. Mike advises companies to build the right "primitives" — foundational architectural layers that allow both humans and agents to interact seamlessly, enabling gradual evolution rather than bolted-on AI features.
Ami addresses the challenge of balancing frontier exploration with providing a stable user experience. Anthropic's approach is to "meet everyone where they are," encouraging numerous "shots on goal" and parallel experiments. This allows them to discover what works before consolidating into a more stable product. Mike adds that success often comes from teams with high conviction and robust underlying infrastructure (like shared memory systems) that allow different experimental products to feel complementary rather than disconnected.
The panel also touches on how to identify success and avoid "capability blind spots." Beyond traditional product-market fit metrics, Mike highlights the importance of "parking" projects that don't work with current models but revisiting them as new models are released. He gives an example of an internal computer-use product that was initially "so bad" but saw a breakthrough with Claude 3.7, demonstrating that models can improve in unexpected ways.
Looking a year ahead, Ami hopes for increased individual and small team empowerment, making building easier for everyone and magnifying the impact of builders. Mike's aspiration is to close the gap between the capabilities of AI models and how most people use them. He envisions a future where advanced, multi-agent setups are not just for "Claude-pilled software engineers" but are accessible and democratized, empowering a broader range of users in their professional and personal lives.