Maximizing Option Value Over Minimizing Regret

Asha Sharma believes we're entering an agentic society where the marginal cost of good output approaches zero, creating exponential demand for productivity that can only be met through AI agents. This fundamental shift is changing how organizations operate and how products are built.

In Sharma's view, we're moving from "product as artifact" to "product as organism" – living systems that continuously improve through interaction. Traditional static products are giving way to products that think, live, and learn, with their metabolism (ability to ingest data, digest rewards, and create outcomes) becoming the new competitive advantage. This requires a fundamental rethinking of how we build and measure products.

The most successful AI-implementing organizations share key patterns: they make everyone AI-fluent in daily workflows, apply AI to existing processes to demonstrate impact, and then use AI to inflect growth through improved customer experiences or new capabilities. Companies that fail typically pursue AI for AI's sake without a coherent blueprint or proper measurement systems.

Sharma sees a shift from GUI-focused interfaces to code-native interfaces that connect better with LLMs. Product makers need to rewire their thinking around composability rather than canvas design, considering how agents will read and interact with their products. This doesn't mean chat will be the only interface – email, docs, and other artifacts will remain important composable pieces.

For product teams, Sharma recommends organizing around "seasons" rather than rigid roadmaps. These seasons are defined by secular changes in the industry (like the current "rise of agents") and can last anywhere from three months to a year. Teams align on the ethos of what's changing, customer problems to solve, and a north star metric, then create loose quarterly OKRs with 4-6 week goals for squads. Crucially, they leave slack in the system not just for unplanned work but for "the slope" – continuously thinking about how to disrupt their own platform.

The rise of AI is also changing team structures. Sharma predicts the org chart will become the "work chart" with fewer hierarchical layers as agents handle more tasks. She advocates for "full stack builders" who can move quickly through the entire product development loop rather than specialists working in lanes – focusing on the loop, not the lane.

For leaders and ICs alike, this means developing broader skills across functions, becoming comfortable with continuous feedback and observability as culture, and focusing on throughput rather than traditional organizational constructs. The most valuable skill is becoming a polymath who can rapidly iterate through the entire product development cycle rather than specializing in a narrow discipline.

Practical Implications for Decision Making

Sharma's perspective suggests several key shifts in how we should approach product development:

Invest in post-training over pre-training - With the explosion of available models, you'll get more leverage economically by fine-tuning existing models for specific outcomes rather than building from scratch.
Design for composability over UI - Focus less on pixel-perfect interfaces and more on how your product components can be composed and used by agents.
Plan in "seasons" not rigid roadmaps - Align teams around the current technological wave rather than fixed timelines, with loose quarterly goals and regular reassessment.
Build for the loop, not the lane - Optimize for the entire feedback cycle rather than functional excellence in one area.
Leave slack for "the slope" - Reserve capacity not just for unplanned work but for continuously rethinking how you might disrupt your own platform.

The most successful builders in this new paradigm will be those who can rapidly iterate through the entire product development cycle, working across traditional functional boundaries to create products that continuously learn and improve.