Platform Success Requires Invisible Infrastructure

The shift from product as artifact to product as organism reflects a fundamental change in how AI-powered products evolve and create value through continuous learning and adaptation.

Product as Organism: The New Paradigm

• Products are evolving from static artifacts to living organisms that improve through continuous data ingestion and learning
• "All of a sudden these are these living organisms that just get better with the more interactions that happen"
• This represents "the new IP of every single company" - a proprietary learning loop rather than just features
• Success depends on the "metabolism" of a product team to ingest data, digest rewards models, and create outcomes

The Signals Loop vs. Traditional Lanes

• "It's all about the loop not the lane" - focus on the continuous improvement cycle rather than functional silos
• The loop consists of:
  • Ingesting user interaction data
  • Fine-tuning models based on that data
  • Measuring improvements in outcomes
  • Iterating rapidly
• "Feedback becomes continuous and observability becomes the culture"
• Functions start to blur as the loop becomes the organizing principle

Post-Training: The New Competitive Advantage

• "We will see just as much money spent on post-training as we will on pre-training and in the future more on post-training"
• Once models reach about 30 billion parameters, it becomes more economical to fine-tune existing models than to train new ones
• Companies can gain leverage by:
  • Using their proprietary data to fine-tune models
  • Creating synthetic data for training
  • Designing effective rewards systems
  • Implementing rigorous evaluation frameworks

Planning in the AI Era

• Traditional roadmapping is challenging when technology changes so rapidly
• Microsoft uses a "seasons" approach rather than rigid timelines:
  • Seasons are defined by secular changes in the industry
  • Current season: "The rise of agents"
  • Seasons can last 3-12 months depending on market evolution
• Implementation approach:
  • Align on the "ethos" of the current season
  • Set loose quarterly OKRs that ladder up to seasonal goals
  • Teams operate in squads with 4-6 week goals
  • Leave slack in the system for both unplanned work and "the slope" (long-term direction)

The Rise of the Full-Stack Builder

• AI is driving a renaissance of the polymath builder
• "I really believe in the concept of a full stack builder"
• Traditional organizations have too many handoffs:
  • "10 steps to launch a product"
  • "5+ functions"
  • "6-7 layers"
  • "500 different touch points to get a product out"
• This structure cannot keep pace with AI's rapid evolution
• AI-native companies are succeeding with smaller, cross-functional teams that can iterate rapidly

The Agentic Society

• "We're approaching this world in which the marginal cost of the good output is approaching zero"
• This will drive "exponential demand for productivity and outputs"
• Scaling to meet this demand requires agents - both embedded in tools and embodied as assistants
• Organizational implications:
  • "The org chart starts to become the work chart"
  • Tasks and throughput become more important than hierarchy
  • Fewer management layers will be needed
  • Workers will have their own "agent stack" that expands their capabilities

Platform Success Principles

• What makes platforms win isn't flashy features but foundational elements:
  • Reliability - consistent performance
  • Privacy - protecting sensitive information
  • Availability - being there when needed
  • Data residency - keeping data where it belongs
  • Performance - speed and efficiency
• "It's not all the features for the platform that matters, it's the data residency... it's the availability, it's the reliability"