Products Evolve From Artifacts to Organisms

From Product as Artifact to Product as Organism: The New AI Product Paradigm

In the AI era, products are evolving from static artifacts to living organisms that continuously learn and improve through data. This represents a fundamental shift in how we build and measure product success.

The Product Metabolism Loop

• Products are becoming "living organisms that get better with more interactions"
• The key competitive advantage is no longer just shipping features but:
  • How quickly you can ingest data from user interactions
  • How effectively you digest and update your rewards model
  • How rapidly you can tune for specific outcomes (price, performance, quality)
• "I think this is the new IP of every single company"
• The focus shifts from "the lane" (your specific function) to "the loop" (the entire feedback cycle)

Post-Training is the New Pre-Training

• We're moving from pre-training (building models from scratch) to post-training (fine-tuning existing models)
• "I believe we will see just as much money spent on post-training as pre-training, and in the future more on post-training"
• Economic logic: Once models reach ~30B parameters, it's more efficient to fine-tune than build from scratch
• 50% of developers are now fine-tuning models rather than using them off-the-shelf
• Companies can gain leverage through:
  • Using their proprietary data
  • Synthetic data generation
  • Creating custom reward systems
  • Optimizing for specific outcomes

Planning in the AI Era

• Traditional 6-month planning cycles are too rigid for the pace of AI development
• Microsoft uses a "seasons" approach instead of fixed planning cycles:
  • Seasons are defined by secular changes in the industry or customer needs
  • A season might last 3 months, 6 months, or a year
  • Current season: "The Rise of Agents"
• Planning components:
  • Align on the "ethos" of the current season
  • Set loose quarterly OKRs that ladder to the season's goals
  • Teams operate in squads with 4-6 week goals
  • Leave slack in the system for unplanned work and "the slope" (long-term direction)
  • "We have to continuously be thinking about how we're going to disrupt the platform"

The Rise of the Full-Stack Builder

• Traditional organizations have too many handoffs for the AI era:
  • "It takes probably 10 steps to launch a product"
  • "5+ functions, 6-7 layers = 500 different touch points"
  • This is "insufficient" when there are "500 models available a week"
• The polymath/full-stack builder is having a renaissance:
  • Must understand efficiency/cost, rewards design, UI/UX
  • Functions are blurring as feedback becomes continuous
  • "It's all about the loop not the lane"
• This pattern is visible in AI-native companies and even 50-year-old enterprises

The Coming Agentic Society

• "We're approaching a world in which the marginal cost of good output is approaching zero"
• This will drive exponential demand for productivity and outputs
• The way to scale to this demand is with agents (both embedded and embodied)
• 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
  • Employees will have their own "agent stack" that expands their skillset