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Lenny's 🏦 Vault

Earlier Organization-Wide Visibility Would Have Accelerated Adoption

by Tomer Cohen on December 4, 2025

LinkedIn's Full Stack Builder Model: Transforming Product Development for the AI Era

LinkedIn's strategic shift to a "Full Stack Builder" model demonstrates how organizations can fundamentally reimagine product development to embrace AI capabilities and respond to accelerating market changes.

Situation

  • Organizational context: LinkedIn, a mature tech company with established processes and specialized roles
  • Market pressure: Skills required for jobs changing by 70% by 2030
  • Development challenges: Product development had become increasingly complex with specialized roles, multiple review stages, and organizational silos
  • Competitive necessity: The "time constant of change" had become greater than the "time constant of response"
  • Legacy constraints: Established code bases, design systems, and organizational structures

Actions

Platform Transformation

  • Rearchitected core platforms to make them AI-compatible
  • Created composable UI components with server-side support
  • Customized third-party AI tools to work with LinkedIn's specific systems
  • Built integration layers between existing code and AI development tools

AI Agent Development

  • Created specialized AI agents for specific functions rather than general-purpose tools:
    • Trust agent: Evaluates potential vulnerabilities and harm vectors
    • Growth agent: Analyzes growth opportunities and critiques ideas
    • Research agent: Trained on member personas and support tickets
    • Analyst agent: Enables natural language querying of LinkedIn's data
    • Maintenance agent: Automatically fixes failed builds
    • QA agent: Handles quality assurance tasks

Organizational Redesign

  • Introduced "Full Stack Builder" as an official career path and title
  • Replaced APM program with "Associate Product Builder" program
  • Reorganized teams into smaller, cross-functional "pods" focused on missions
  • Modified performance reviews to evaluate AI fluency and cross-functional capabilities
  • Created incentives and recognition for those embracing the new model

Cultural Transformation

  • Celebrated early wins and showcased success stories
  • Enabled career transitions (e.g., researcher becoming a growth PM)
  • Emphasized permission to experiment without waiting for formal reorganization
  • Communicated vision while showing continuous progress
  • Encouraged sharing of effective AI tools and techniques

Results

Early Outcomes

  • Teams saving hours of work per week
  • Higher quality insights and discussions
  • Maintenance agent handling approximately 50% of failed builds
  • Designers pushing code directly (previously unprecedented)
  • Increased agility and adaptability in product development
  • Top performers embracing and finding most success with the model

Challenges

  • Off-the-shelf AI tools rarely worked without significant customization
  • Initial attempts to give AI access to all company knowledge failed
  • Different teams gravitated to different tools, creating standardization challenges
  • Some employees preferred specialization over becoming full-stack builders
  • Change management proved as critical as the technology itself

Key Lessons

  • Invest in platform readiness: Success requires rearchitecting systems to be AI-compatible, not just adding AI tools on top.

  • Customize for context: Generic AI tools don't work well with specific company systems; significant customization is necessary.

  • Curate knowledge carefully: Don't just give AI access to all company information; carefully select and clean the knowledge base.

  • Focus on culture, not just tools: Providing tools is necessary but insufficient; cultural change requires incentives, examples, and celebration of wins.

  • Start with early adopters: Begin with motivated teams who will provide feedback and create success stories that inspire others.

  • Embrace the journey: Position the transformation as continuous progress rather than a fixed end state.

  • Communicate broadly early: One key learning was that keeping work to a small team initially limited visibility; providing early access to tools and showing progress would have accelerated adoption.

  • Align individual and organizational incentives: Frame the change as beneficial both for the company's competitiveness and for individuals' career development.

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