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

Episode Overview

• The episode addresses the urgent need for workforce adaptation in response to AI, which is projected to change 70% of job skills by 2030.
• LinkedIn's Chief Product Officer, Tomer Cohen, introduces the "Full Stack Builder" model, a new framework using AI agents to empower any employee to take an idea from insight to launch.
• The discussion details the practical application of this model, including specific AI agents for coding, maintenance, and QA, highlighting the critical importance of training AI on curated, high-quality data.
• A central theme is that successful AI integration is less about the technology itself and more about driving cultural transformation through deliberate change management, grassroots adoption, and new incentive structures.

Key Concepts

• Full-Stack Builder (FSB) Model: A framework designed to empower any employee—including PMs, designers, and engineers—to manage the entire product lifecycle from idea to launch, thereby breaking down traditional organizational silos.
• Human-Machine Collaboration: The model moves beyond rigid processes to a fluid interaction between humans and a suite of specialized AI agents that augment and accelerate creative and technical work.
• Expanding AI Agents: Development is progressing from simple coding assistants to more advanced agents for maintenance (which already handles nearly 50% of failed builds at LinkedIn), quality assurance, and eventually the creative "idea to design" phase.
• Curated Data for AI Training: A major learning is that AI performs poorly when given raw access to an entire knowledge base; its effectiveness depends on being trained with clean, curated "golden examples" of best practices.
• Cultural Transformation as the Core Challenge: The most significant hurdle in this new era is not technological but organizational. Success requires a deliberate shift in company culture, fostered by new incentives, training programs, and celebrating the wins of early adopters.

Quotes

• At 0:02 - "When we look at the skills required to do your job, by 2030, they will change by 70%." - Tomer Cohen sets the stage by highlighting the massive shift in job skills driven by AI, making adaptation a necessity.
• At 0:13 - "Go back to the drawing board and reimagine what it means to be building." - Cohen explains that in this new AI-driven era, companies must fundamentally rethink their core processes to remain competitive.
• At 0:27 - "The goal itself is to empower great builders to take their idea and to take it to market regardless of their role in the stack and which team they're on." - Cohen clarifies the mission behind the Full Stack Builder model, which is to break down traditional silos between functions.
• At 0:35 - "It's really a fluid interaction between human and machine." - Cohen describes the new dynamic where AI agents augment human capabilities, rather than simply following a rigid, step-by-step process.
• At 0:55 - "It doesn't work this way." - Cohen points out a common failure mode where companies simply deploy AI tools and expect employees to adopt them without proper incentives, motivation, or examples.
• At 27:13 - "We're close to 50% of all those builds being done by the maintenance agent." - Cohen shares a specific metric on the success of their AI maintenance agent in automatically fixing failed code builds.
• At 27:24 - "You can still go and finish your coffee before you have to go and redo the build again." - Cohen colorfully illustrates the productivity benefit of having an AI agent handle routine fixes for failed builds.
• At 28:25 - "It's not great to just give it access to your drive and say, 'Reason all over this knowledge base.' It actually does a very poor job." - Cohen explains a major early learning that AI performance is poor without curated and clean training data.
• At 32:11 - "(Success is) a function of experimentation volume multiplied by quality... divided by the time it takes to actually pull them out, like idea to launch." - Cohen shares his formula for measuring the overall impact and efficiency of the product development process.
• At 36:15 - "If you're waiting for a reorg or a declaration to start building differently, you're waiting too long... Here's a permission from me to just not wait and just go." - Cohen emphasizes that individuals should take initiative to change how they work rather than waiting for top-down permission.
• At 36:27 - "Becoming is better than being." - Cohen shares his life motto, which ties into the idea of continuous growth and embracing the journey of improvement.

Takeaways

• Embrace proactive experimentation with AI tools. Do not wait for official company-wide rollouts or top-down mandates; take the initiative to lean in, experiment, and start building differently now.
• Prioritize curating high-quality data for AI. Instead of granting AI access to all company knowledge, focus on identifying, cleaning, and feeding it "golden examples" of best practices to ensure effective learning and performance.
• Lead with cultural change, not just technology deployment. The success of AI integration depends on motivating people. Create new incentives, provide clear examples, and celebrate early adopters to drive a grassroots movement.
• Adopt a "becoming" mindset for continuous growth. In an era of constant change, view professional development as a perpetual journey of adaptation and learning rather than aiming for a fixed, final skill set.
• Use AI to break down functional silos. Leverage AI tooling to empower individuals and teams to handle tasks across the entire product lifecycle, bridging the gaps between product, design, and engineering for faster, more integrated workflows.
• Rethink success metrics for the AI era. Measure product development efficiency by focusing on the speed and quality of iteration, using a formula like (Experimentation Volume x Quality) / Time-to-Launch.