ChinAI #114: Tencent's Manufacturing Strategy
Plus, JeffJots on middleware
Greetings from a world where…
众人拾柴火焰高 [When everyone adds fuel, the flame burns brighter] zhong4ren2 shi2 chai2 huo3yan4 gao1
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Feature Translation: Tencent and Manufacturing?
Thanks everyone for taking part in the second round of ChinAI Around the Horn. Another close vote among 1, 4, 3, and 9. I chose #1 — “Tencent, a Manufacturing Re-evaluation” — since it had the most support among the choices that paying subscribers voted for (and tied for most votes overall).
Context: in-depth article on Tencent’s effort to transform China’s manufacturing sector from 机器之能/jiqizhineng (Synced), which has been the source for many previous ChinAI issues on smart manufacturing and the industrial Internet: #58, #70, #78.
Wu Xiaobo, a financial writer, predicted in 2017 that in the next few years, 80% of small and medium-sized enterprises in traditional manufacturing will go bankrupt
These companies have two choices: 1) transform via informatization (think robotic arms and industrial Internet); 2) implement the “servicification” of manufacturing — for instance, if you’re a construction machinery manufacturer, you also have to provide maintenance services for the equipment after the initial sale.
Tencent is working at multiple entry points in the manufacturing chain, including: marketing, data collection and monitoring of equipment, industrial vision in production lines, and ecosystem partners in independent software vendors.
Mini case studies of all 4 vectors:
Marketing: WeChat Enterprise/WeChat Work (similar to Slack) helps LingLong Tire target hundreds of millions of users directly, rather than going through 400 dealers and 50 or 60 automobile factories. Linglong was ranked first among Chinese companies in the tire original equipment list, though Michelin and four other foreign companies placed abote it (insert bad headline about the race for tire dominance).
ICT monitoring and data collection on equipment: SANY Heavy Industry can complete the monitoring of 400,000 engineering equipment, reaching early warning of equipment failures 6.5 hours in advance, and the early warning accuracy rate is 87%.
Industrial vision opportunities in production: Tencent Cloud’s work with China Star Optoelectronics — the first domestic AI recognition project for LCD panel defect types, ADC (Auto defect Classification, has been repeatedly brought up as a typical example of industrial Internet intelligent manufacturing.
Ecosystem partnerships with independent software vendors: "The service provider does something similar to a bricklayer, building the raw materials provided by Tencent into a house where businesses can live." Zhu Ning, the founder of Youzan, once described the relationship between the WeChat ecosystem and Youzan.
Article posits some intriguing differences between Germany and China re: their approach to manufacturing transformation:
In Germany, top companies within the manufacturing industry led the charge; in China, the existence of major consumer Internet companies dictates that they will play an important role in the transformation of manufacturing.
Germany more focused on optimizing production processes, whereas Chinese companies see more opportunities in improving the range of manufacturing services —to me, this one was more of an unproven generalization but an interesting theory
Longtime readers are probably tired of me harping on this point, but read the full translation and you’ll find that there are zero references to the U.S. in this entire article. In contrast, references to Japan and Germany abound. AI is not a two-player game.
A lot more details in the FULL TRANSLATION: Tencent, A Manufacturing Re-evaluation
Middleware — JeffJots
Back when Bill Simmons used to write articles, instead of spouting bad anti-Lakers takes on podcasts, he used to end his mailbag columns with an outrageous-crazy-funny email from a reader followed by: “Yup…these are my readers.”
After last week’s issue, in which I suggested “middle platform” as a translation for 中台, many readers corrected me that “middleware” was the better fit. Thanks especially to Noah and Yorwba for their insights. Middleware is software that connects the frontend and backend systems via APIs and the like. Alibaba did not invent the concept, as I implied, but they might have popularized the concept in China. Yup…these are my readers.
I had encountered the term a couple times before in the course of dissertation research, and this discussion sparked me to look back through some notes, so let’s get into another edition of JeffJots:
From a Casper and Whitley 2004 Research Policy article: They find that Sweden is extremely successful in the “middleware” sector, which differs from more traditional software that comes in ready-for-consumer-use programs. Instead, middleware technologies help link basic architectures to standard application software. For typical middleware products, think: software that transforms the content of web servers into formats suitable for mobile phones, or secure payment systems used in e-commerce applications.
Why is Sweden so successful in this sector? Why is the UK not as successful, even though it is very strong at innovating in standard software? Per Casper and Whitely’s explanation it’s because Sweden’s firms (led by Ericsson) developed and shared common technical standards, which is especially important in a sector like middle ware, which requires significant coordination. They tell a similar story about Germany’s success in the life sciences, in which Germany dominates the “middleware” of biotech (platform biotechnology) but is less successful in developing new therapeutic drugs.
This article is cited in Jingjing Huo’s book How Nations Innovate (p. 73), in which Huo argues that a nation will be successful at a particular type of innovation based on the nation’s “variety of capitalism.” Coordinated market economies, like Germany and Sweden, will be better at process innovation, as the middleware case shows. Liberal market economies, like the U.S. and the UK, will be stronger in product innovation.
What does this all mean? There’s a lot to unpack here and a lot of contention over “varieties of capitalism,” but here’s one clear takeaway: I often get asked in events about whether U.S.-China tech competition is a zero-sum game. What the above examples of specialization, like Sweden in middleware, remind us of is: Trade 101. Yes, of course there are strategic aspects of trade and there will be sensitive areas where competition will be of a “you-win, I-lose” nature, but these are the exceptions, not the rule. The baseline and default is positive-sum.
ChinAI Links (Four to Forward)
It’s impossible to capture what this story is about. The New Yorker’s short description — “Immigrant struggles in America forged a bond that became even tighter after my mother’s A.L.S. diagnosis. Then, as COVID-19 threatened, Chinese nationalists began calling us traitors to our country” — doesn’t do it justice. But I guess I’d say it’s a story of survival — in the face of diseases of all forms: A.L.S. and COVID, racism, and Chinese propaganda.
Listen to the author, Jiayang Fan, talk about this piece on the Longform podcast, which also highlights some of her other great writing.
Should-read: What’s Behind Technology Hype
Jeffrey Funk for Issues in Science and Technology pierces though the hype: “After years of hype about AI, some traditionally optimistic voices are finally beginning to temper their exuberance. A March 2019 article in IEEE Spectrum argued that Watson, IBM’s AI division, had “overpromised and underdelivered” on personalized health care applications, and shortly thereafter IBM pulled Watson from drug discovery. An April 2019 article in Technology Review went further with a title “This Is Why AI Has Yet to Reshape Most Businesses.” My forthcoming article in IEEE Spectrum (“Why Projections for AI’s Economic Benefits Are Overly Optimistic”) demonstrates that the most-well-funded AI start-ups are not targeting productivity-enhancing applications, and many are likely incurring huge losses.”
Should-read: State of AI 2020 Report
For the third year, Ian Hogart and Nathan Benaich review the state of AI. Had a chance to review the report before they published it, and they have a lot of good stuff on China’s AI ecosystem as well.
On September 8, 2020, the National Development and Reform Commission, Ministry of Science and Technology, Ministry of Industry and Information Technology, and Ministry of Finance published this document: 'Guiding Opinions on Expanding Investment in Strategic Emerging Industries and Cultivating Strengthened New Growth Points and Growth Poles.” It’s been jointly translated by DigiChina and CSET: Elsa Kania, Ngor Luong, Caroline Meinhardt, Ben Murphy, Dahlia Peterson, Helen Toner, Graham Webster, and Emily Weinstein, and edited by Ben Murphy and Graham Webster.
Thank you for reading and engaging.
These are Jeff Ding's (sometimes) weekly translations of Chinese-language musings on AI and related topics. Jeff is a PhD candidate in International Relations at the University of Oxford and a researcher at the Center for the Governance of AI at Oxford’s Future of Humanity Institute.
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