Why LinkedIn is turning PMs into AI-powered "full stack builders” | Tomer Cohen (LinkedIn CPO)

Dec 4, 2025 1h 7m 26 insights Episode Page ↗
Tomer Cohen, LinkedIn's CPO, discusses their 'Full Stack Builder' model, a new approach to product development that leverages AI to empower anyone to take ideas from concept to launch. This aims to increase agility and adapt to the accelerating pace of technological change.
Actionable Insights

1. Reimagine Building First Principles

To stay competitive, companies must revisit first principles and reimagine their approach to building products, as the pace of change is faster than the ability to respond.

2. Prioritize AI Change Management

Actively manage change by building incentive programs, providing motivation, and showcasing successful examples to foster a culture where people see the value and want to adopt AI tools.

3. Invest in Platform, Tools, Culture

Successfully implementing AI-driven product development requires investment in re-architecting core platforms for AI, building custom AI tools/agents, and fostering a supportive culture for adoption.

4. Empower End-to-End Builders

Empower builders to develop experiences from idea to launch, combining skills across traditional domains, rather than segmenting the process into complex sub-steps and specialized roles.

5. Focus on Core Human Traits

Builders should prioritize developing vision, empathy, communication, creativity, and especially judgment, as these human traits are crucial and hard to automate.

6. Automate Non-Core Development Tasks

Actively work to automate all product development tasks that do not require core human traits, freeing up builders for higher-value, strategic work.

7. Foster Human-AI Fluid Interaction

Shift from sequential product development to a fluid interaction between human builders and AI tools, integrating AI at every step for efficiency and quality.

8. Re-architect Platforms for AI

Re-architect core platforms and codebases (e.g., building composable UI components) to enable AI to reason over them effectively, as off-the-shelf AI tools often require significant customization.

9. Customize Third-Party AI Tools

Do not expect third-party AI tools to work out-of-the-box; plan to customize them extensively and collaborate with vendors for effective integration with your unique tech stack.

10. Build Custom AI Agents

Develop specialized AI agents (e.g., trust or growth agents) trained on your company’s unique data, context, and know-how, as generic agents cannot capture specific organizational needs.

11. Curate AI Training Data

Carefully curate specific ‘gold examples’ and define the context window for AI training, rather than giving access to all internal data, to avoid hallucinations and misinterpretations.

12. Automate Code-to-Launch Processes

Invest in AI agents for the ‘code to launch’ phase, including coding, maintenance (for failed builds), and QA agents, to significantly accelerate development and reduce manual effort.

13. Adopt Small, Nimble Pods

Organize into small, cross-functional ‘pods’ of full-stack builders who can flex across different roles, focusing on specific missions for a quarter to increase velocity and nimbleness.

14. Adjust Performance Reviews for AI

Align performance expectations, hiring criteria, and evaluation processes to reward ‘AI agency and fluency,’ encouraging employees to actively use and improve AI tools.

15. Pilot and Showcase Successes

Implement pilot programs to demonstrate tangible successes of AI-driven workflows, providing concrete examples that inspire broader adoption within the organization.

16. Develop Full-Stack Training Programs

Institute formal training programs (e.g., Associate Product Builder program) to teach new hires how to code, design, and product manage, fostering a new generation of full-stack builders.

17. Act Without Formal Directives

Don’t wait for a formal reorg or declaration; proactively use or build new tools, demonstrate a full-stack builder mindset, and prove capabilities through action.

18. Maintain High Visibility During Pilots

Even when starting with a small core team for AI initiatives, ensure high visibility across the organization by regularly sharing progress, tools, and early successes to foster awareness and engagement.

19. Be Patient with AI Transformation

When transforming a large organization with AI, be ambitious about the goal but patient and thoughtful about implementation, understanding it requires significant upfront investment and time.

20. Allocate Resources for AI Infrastructure

Be fully aware of the necessary investment in re-architecting platforms and customizing AI tools, and ensure adequate resources are allocated to these foundational efforts for successful outcomes.

21. Declare AI Vision, Continuous Progress

Clearly communicate the long-term vision for AI adoption and emphasize that it’s a continuous journey of improvement, not a fixed end state, to align the organization and foster a growth mindset.

22. Over-communicate AI Progress

Over-communicate not only the vision for AI transformation but also the ongoing progress, potentially using KPIs or OKRs, to maintain transparency and hold leadership accountable.

23. Proactively Engage with AI

Individuals should proactively engage with AI tools and new building methods within their current role or seek opportunities in organizations that are at the cutting edge of AI-driven development.

24. Continuously Improve Your Craft

Top talent consistently strives to enhance their skills and stay at the cutting edge of their craft, which is crucial for adapting to rapid changes in job requirements.

25. Highlight Cross-Functional Transitions

Showcase examples of employees leveraging AI tools to transition into new roles or functions, demonstrating career growth opportunities enabled by full-stack building.

26. Leverage AI for Empowerment

View technology, especially AI, as a tool for empowerment and meritocracy, enabling individuals to achieve more and contribute based on their abilities rather than their specialized role.