The Full-Stack Builder Model
Empower one builder to take an idea to market end-to-end, regardless of role or team
- Difficulty
- Expert
- Time to result
- ~months to results
- Steps
- 4
- Confidence
- 95%
A production model that collapses a bloated, micro-specialized product process back into a single empowered builder who moves an idea from insight to launch. It rests on three sequenced investments — platform, tools/agents, and culture — where the human owns five traits and everything else is automated. Built and piloted at LinkedIn to make a large org as nimble and adaptive as an AI-native startup.
Origin
Developed by Tomer Cohen, Chief Product Officer at LinkedIn, who replaced the APM program with an Associate Product Builder program and created a formal 'full-stack builder' title and career ladder.
Core principles
- 01The work itself is not complex; the process became complex through accumulated sub-steps and micro-specialization
- 02Change is now happening faster than organizations can respond — the model must match the pace of change to the pace of response
- 03Platform and tools are prerequisites but not sufficient; culture is the decisive investment
- 04Smaller, mission-focused pods beat large functionally-siloed teams
- 05It is a mindset shift, not a title change — 'becoming' over 'being'
How to run it
- 1
Diagnose your process and organizational complexity
Map how each core step (research, spec, design, code, launch) has expanded into many sub-steps (10-15 research sources, design/privacy/security reviews) and how each sub-step spawned a specialized function. Visualize the combined process + org complexity so people see the whole, not just their slice.
Pro tip Build a single diagram overlaying process complexity and org complexity — people working on one narrow piece are mind-blown when they zoom out.
- 2
Invest in the platform first
Re-architect your core codebase and design system so AI can reason over it — composable UI components, server-side structure, and integration layers for external coding/design tools. Off-the-shelf tools never work on a large legacy stack without this layer.
Pro tip Work in near-alpha mode directly with vendors (Cursor, Devin, Figma, Copilot) to adapt their product to your stack and vice versa.
Watch out Do not expect any third-party tool to work off the shelf on a mature codebase. 'It never works.'
- 3
Build customized, single-job agents
Automate every step outside the five human traits by building narrow, gradeable agents (trust, growth, research, analyst, product-jam). Have the head of craft for each area build their own agent so domain know-how is embedded. Plan an orchestrator layer to let agents call each other later.
Pro tip Start with discrete single-purpose agents you can rate and grade before building the orchestrating layer that masks them behind one interface.
Watch out Different teams gravitate to different tools; you will need to deliberately converge rather than let tool sprawl persist.
- 4
Invest heavily in culture and change management
Treat adoption as an internal product with early-adopter feedback. Run a pod pilot, celebrate wins in all-hands, embed AI agency into hiring and performance reviews, and give people explicit permission to start before any formal re-org.
Pro tip Rewire performance evaluation (360s, bi-annual reviews, hiring criteria) toward full-stack behavior — people change fastest when they know how they're being rated.
Watch out Rolling out tools and expecting adoption 'doesn't work this way' — only ~5% of cutting-edge talent adopts unprompted; the majority needs active change management.
In the wild
The team that launched semantic people search and semantic job search used the FSB tools so PMs could build their own dashboards without waiting for design resources; designers on the team began pushing PRs, which had never happened before.
→ Faster shipping with PMs and designers flexing across traditional function lines.
Cohen moved his direct reports from functional leaders to product-area leaders working across the stack, ran 360 reviews where PMs were rated by designers, and let design teams lean in before official GA rather than waiting.
→ Top talent became the heaviest users, producing higher-quality output and stronger buy-in.
Common mistakes
Rolling out agents and expecting automatic adoption
Giving people tools is necessary but not sufficient. Without incentive programs, visible success stories, and change management, only the ~5% cutting-edge talent adopts and the transformation stalls.
Building the core team on the side with no org visibility
Cohen worked closely with a small core team while the wider org kept asking what was happening; in retrospect he would have shown early tools and progress broadly in the flow rather than keeping it siloed.
Skimping on platform and tool customization
If you don't invest in re-architecting your platform and customizing tools, you get vanilla generic agents that never work on your legacy stack — there is no successful outcome without the upfront investment.
Is it for you?
Best for
Product and engineering leaders at scaled companies with legacy codebases who want to rebuild how product is made around AI
Not ideal for
AI-native startups with no legacy code or structure — they already operate as full-stack builders by default and don't need the transformation
From the transcript
“We call it the full-stack builder [music] model. The goal itself is to empower great builders to take their idea and to take it to…”
“there's really three components that we're working on. One is platform, the second one is the tools and the agents and lastly is the culture.”
“the platform for us as an example is re-architecting all of our core platform so AI can reason over it”
“the head of craft for every area is building their own agent”
“It's not enough to give them the tools. You have to build incentive programs, the motivation, the examples to how you do it. I see…”
From the episode
Why LinkedIn is turning PMs into AI-powered "full stack builders”
Tomer Cohen (LinkedIn CPO)