HANDBOOK
Running R&D in the AI era — lessons and thinking from the work
What happened, what I learned, and what I think now — from running global R&D organizations and portfolios, in six parts. Each entry opens with the decision it helps with and ends with where it doesn't apply.
0 Start here
1 Why global R&D sites
- 1-1 Why global R&D at all Coming
- 1-2 Labs, sites, partners: what global R&D actually is Coming
- 1-3 What Should a Global R&D Center Be Given to Own?
- 1-4 HQ ships products, sites look ahead — how that split happened Coming
2 Choosing and allocating projects
- 2-1 Which lab gets which project Coming
- 2-2 The open-vs-strategic mix wasn't planned Coming
- 2-3 Aligning with HQ raised commercialization — and what it cost Coming
- 2-4 Filling the gaps from outside: splitting the work with open innovation Coming
3 From advanced tech to product
- 3-1 From 10% to 50% Commercialization: How I Redesigned Overseas Lab Advance…
- 3-2 Designing a program from scratch — Global No. 1 Coming
- 3-3 Selling the long game: getting federated learning into a voice assistant Coming
- 3-4 The 2–3-year clock: what it shows you, and what it hides Coming
4 Running a site
- 4-1 Twenty projects, 500 people, one page Coming
- 4-2 When a small site gets there first — the world-first MDFPP Coming
- 4-3 Cross-cultural collaboration, written down Coming
- 4-4 Growing local leaders Coming
- 4-5 When geography limits the work — regulation, security, standards Coming
5 Working with HQ and executives
- 5-1 What goes in the monthly executive report Coming
- 5-2 How to measure a lab Coming
- 5-3 Portfolio management: cutting is harder than adding Coming
- 5-4 Building up technology, paying down tech debt Coming
6 Running R&D in the AI era
- 6-1 Running an AI team of one — what building publish, verify and audit taught me Coming
- 6-2 R&D that logs its misses — forecasts and verdicts Coming
- 6-3 When simulation catches up with reality — sim-to-real Coming
- 6-4 When an AI agent sits next to every researcher Coming