Full Stack Builders Thrive in AI Era

Asha Sharma believes we're entering an era where products evolve from static artifacts into living organisms that continuously improve through data-driven feedback loops. This fundamental shift requires rethinking how we build and organize.

In the AI era, the most successful organizations embrace a metabolism-driven approach where products continuously ingest data, digest rewards models, and create improved outcomes. This creates living systems that get better with each interaction, becoming the true IP of companies. As Sharma explains, "All of a sudden these are these living organisms that just get better with the more interactions that happen."

This shift demands a new type of builder - the polymath who can work across traditional functional boundaries. Traditional organizations require approximately 500 touchpoints across functions and layers to ship a product, which is unsustainable when technologies evolve weekly. Sharma advocates for full-stack builders who can own entire loops rather than specialized lanes: "I think it's all about the loop not the lane here... functions start to blur in future workforces."

For leaders, this means organizing around work rather than hierarchy. The "org chart becomes the work chart" as tasks and throughput take precedence over reporting structures. This requires leaving slack in planning systems - not just for the unplanned but for "the slope" of continuous disruption.

For individual contributors, success means developing broader capabilities across disciplines. The days of specializing in narrow functions are giving way to those who can iterate quickly across the entire product loop. This means understanding efficiency metrics, rewards systems, UI/UX, and technical implementation simultaneously.

Planning horizons are also changing. Rather than rigid roadmaps, Sharma's team organizes around "seasons" defined by secular industry changes. These seasons (like "the rise of agents") provide strategic alignment while quarterly OKRs and 4-6 week squad goals allow for tactical flexibility.

The most successful AI-native companies share common patterns: they make everyone AI-fluent, apply AI to existing processes before creating new ones, and build for composability rather than just interfaces. They avoid the trap of launching disconnected AI projects without measurement frameworks, and they build platform layers that allow swapping technologies as the landscape evolves.

As Sharma puts it, "We have to continuously be thinking about how we're going to disrupt the platform in our thinking and what we need to be investing in to make that possible."