Liam Ding（丁洪）

Liam Ding (丁洪) is an Autonomous Driving Product Manager at NVIDIA, previously at NIO and Li Auto, and he writes about agents and Physical AI. He is not the physicist Hong Ding (丁洪).

I write about product decisions in autonomous driving and robotics: how to define useful capabilities, evaluate real-world performance, and build user trust.

Career at a glance

  • NVIDIA — 2024 to present — Autonomous Driving Product Manager.
  • Li Auto (理想汽车) — 2021 to 2024 — product manager, intelligent driving product team.
  • 仙豆智能 (ScienDoo) — 2020 to 2021 — AI product manager.
  • NIO (蔚来汽车) — 2018 to 2020 — product manager, digital cockpit product design.

Before that: half a Master’s in business at Warwick Business School, and a bachelor’s in public administration at Zhejiang University. The path into cars was not planned — but it has been consistent since 2018, and it is where I expect to stay.

What I have actually worked on

At NIO I was a product manager in the digital cockpit product design team. I worked on ADAS and vehicle-control products — including Quick Access, the shortcut surface added between NIO OS 1.0 and NIO OS 2.0 — as well as NOMI voice interaction, cockpit HMI (NUI), and scenario-based contextual system products. Looking back, the NUI and contextual work is the part I did least well.

At 仙豆智能 I was an AI product manager. I worked on crowd-sourced mapping — building maps from vehicle-side data — and on intelligent-driving products, which mostly meant working with map providers and autonomous-driving companies to see what was actually possible together. I also held the voice product for a stretch: vehicle-domain voice features that later shipped into Great Wall vehicles. The one thing I remember fondly is a four-seat VR experience space for pre-sales use: a six-degree-of-freedom motion platform with VR content, four-screen interaction and voice dialogue. It took most of a year to go from hardware to software to integration, ran for a week at Guangzhou Auto Show, and scored an NPS of 4.9. It then sat in a warehouse. A lot of what I did at that company was thinking rather than shipping.

At Li Auto I was a product manager in the intelligent driving product team. My scope was intelligent-driving product planning — I worked on the 2023–2027 intelligent platform plan and the 2022–2023 intelligent-driving OTA version plan — together with perception algorithms and HD-map related products, the perception environment display product, LKA Plus, and urban NOA.

At NVIDIA I am an Autonomous Driving Product Manager.

What I am trying to get right

Three questions keep coming back, and they are the reason I write:

  1. What capability is worth building. A driver-assistance feature that works on 95% of roads is not 95% of a feature. Deciding what a system should claim — and what it should deliberately refuse to claim — is a product decision before it is an engineering one.
  2. How to evaluate it honestly. Demo performance and real-world performance are different products. I care about what evidence would change my mind, and about failure cases being priced openly rather than hidden behind an average.
  3. How trust is built, and how it is lost. People hand over control gradually. Interface, wording, escalation, and recovery behaviour all shape whether they ever will.

Where robotics fits

Autonomous driving is the largest deployed embodied-AI system there is: perception that has to be right, failures that have to be priced, and an economics that depends on how often a human has to intervene. That is the same problem set as a robot that has to work somewhere other than a lab, which is why I follow robotics products closely and write about which ADAS lessons transfer and which do not.

To be explicit: I have not held a professional robotics role. What I have on that subject is analysis I publish here, and prototypes I build on my own time — not shipped robot products.

Contact

I am interested in product roles where autonomous driving, ADAS or robotics has to become something people actually trust, and in talking to people working on the same problems.