---
id: "lib-2026-10-09-we-may-not-see-general-purpose-robots-in-our"
type: "library"
title: "We may not see general-purpose robots in our lifetime"
title_original: "李飞飞收购郑昌熙：有生之年，我们可能看不到通用机器人出现"
lang: "ZH"
org: "课代表立正"
source_type: "Podcast"
date: "2026-10-09"
week: "2026-W41"
url: "https://nosov.ai/library/lib-2026-10-09-we-may-not-see-general-purpose-robots-in-our/"
original_url: "https://www.youtube.com/watch?v=y6kRa1vGAwA"
tier: "B"
tier_name: "Useful, limited"
tier_reason: "Long interview with Changxi Zheng, a Columbia computer science professor now at World Labs, giving a reasoned practitioner case on industrial robotics, with argument but little data."
summary_basis: "transcript"
original_minutes: "60"
tags: ["ai technology", "embodied ai", "capability trajectory"]
---

# We may not see general-purpose robots in our lifetime

Original title: 李飞飞收购郑昌熙：有生之年，我们可能看不到通用机器人出现

Tier B (Useful, limited): Long interview with Changxi Zheng, a Columbia computer science professor now at World Labs, giving a reasoned practitioner case on industrial robotics, with argument but little data.

Summary based on the full transcript.

## In short

An hour-long Mandarin interview on the channel 课代表立正, published 9 October 2026, with Changxi Zheng, professor of computer science at Columbia University, co-founder of the robotics simulation start-up SceniX and now full-time at Fei-Fei Li's World Labs. He argues that industrial robots will not follow the language-model curve to a single general model. Factories need policies that are robust and faster than people, and economic specialisation favours task-specific systems. He says no embodied-AI company, including his own, is yet used at scale in real production.

## Key numbers

- 3 of 10: failure rate he calls unacceptable for an industrial robot policy
- 80-90%: share of industrial tasks he does not expect a single foundation model to handle at production reliability soon
- GPT-2 stage: his comparison for where embodied AI stands today

## What it reports

- From running an incubation lab for Tencent in New York: in business, technology is "not the most critical or useful thing, not even the second".
- On academia: evaluation counts output, so resubmitting a paper to new reviewers is "buying a lottery ticket".
- SceniX builds a real-to-sim-to-real workflow: capture a real task cheaply, rebuild it in simulation, generate data, evaluate, then transfer a policy back.
- Industrial policies must be robust (failing 3 or 4 times in 10 runs is unacceptable) and faster than a person, or the ROI does not work.
- Three axes: generalisation, robustness, throughput. He expects no model like ChatGPT to handle 80-90% of industrial tasks at production level soon.
- Citing Adam Smith on specialisation, he argues mass production will keep needing specialised policies; home robots differ because buyers want one machine.
- Limits beyond data: motor control runs well above a game engine's 60 Hz, hardware moves slowly, and the physical world lacks an agreed 3D representation.
- World model, in his definition: given a state and an action, predict the next state. Video prediction alone is not one, and training one needs "god's-eye" experience, a chicken-and-egg problem.
- He rejects the view that robotics must happen in China: Western labour costs and unions make automation more urgent there.

## How they got it

A conversational interview drawing on his research and start-up experience; there is no dataset. The digest uses cleaned auto-captions of the Mandarin audio, which mixes in English terms.

## Limits

- Opinion from one researcher whose company sells a simulation-first approach.
- No figures on deployments, costs or failure rates beyond illustrative examples.
- The video title's framing of an "acquisition" by Fei-Fei Li is not explained in the conversation.

## Why it matters for companies

Manufacturing and logistics managers face many claims of near-general robots. His test is practical: does the robot finish the task reliably and faster than a person at lower cost? By that standard, near-term value lies in specialised automation for defined tasks, with simulation used to train and test before deployment. Budgets built on a general-purpose robot arriving soon carry high risk.

## Key passages in translation

> in terms of business(就商业而言) technology(技术)真的真的不是最 关键或者最有用的东西 甚至都不是第二有用的东西

In terms of business, technology really, really is not the most critical or most useful thing; it is not even the second most useful thing.

> 跑十次可能坏个三次四次对吧 那这无法接受的 然后还有就是需要它足够快

If it runs ten times and fails three or four, that is unacceptable. And it also has to be fast enough.

> 跟人的速度是一样的 其实这个ROI(投入产出比)都是算不过来的对吧 我不如雇一个人去做这件事情

If it works at the same speed as a person, the ROI simply does not add up; I would rather hire a person to do the job.

> 很难有这么一个 像ChatGPT一样的一个model(模型)可以 Generalize(泛化)到做各种各样的 尤其是在工业场景当中的这些工作

It is hard to have a model like ChatGPT that can generalise to doing all kinds of work, especially work in industrial settings.

> 所谓的embodied AI(具身AI)的公司 包括我们自己 就坦率地说 都还没有在真实的生产环境中大规模用起来

The so-called embodied-AI companies, including ourselves, frankly, are not yet being used at scale in real production environments.

> 如果一定要用ChatGPT来举例的话 我觉得就GPT-2这种阶段吧 GPT-3都还没有到的阶段

If I must use ChatGPT as a comparison, I think we are at about the GPT-2 stage, not yet even at GPT-3.

Original: https://www.youtube.com/watch?v=y6kRa1vGAwA