The next frontier for AI in China could add USD 600 billion to its economy – Retail Retreats – The Role of Artificial Intelligence in Sports

The next frontier of AI in China could add USD 600 billion to its economy – Retail Retreats – The Role of Artificial Intelligence in Sports

This edition of the Newsletter from TextileFuture entails three different stories, but two of them treat Artificial Intelligence.

The first feature is by guest authors of McKinsey Consultancy and is entitled “The next frontier for AI in China could add USD 600 billion to its economy”

The second item bears the title of Retail Retreats and it is about a book on the history of Malls in the USA.

The third feature throws some light on the “Role of Artificial Intelligence in Sports”.

Artificial Intelligence is gaining quickly access to manifold areas and thus we have picked this subject for your reading.

The story on the Malls in USA is also quite interesting to read.

We hope that you will call back next Tuesday to the TextileFuture Newsletter and we wish you a successful week ahead.


Here starts the first feature:

The next frontier for AI in China could add USD 600 billion to its economy

By Kai Shen, Xiaoxiao Tong, Ting Wu, and Fangning Zhang from McKinsey Consultancy.

Kai Shen and Ting Wu are partners in McKinsey’s Shenzhen office, and Xiaoxiao Tong is a consultant in the Shanghai office, where Fangning Zhang is a partner.

The authors wish to thank Forest Hou, Joanna Mak, Tamim Saleh, Christoph Sandler, Alex Sawaya, Florian Then, Joanna Wu, Xiaolu Xu, and Jeff Yang for their contributions to this article.

By 2030, AI could disrupt transportation and other key sectors in China, adding significant economic value—but only if strategic cooperation and capability building occur across multiple dimensions.

In the past decade, China has built a solid foundation to support its AI economy and made significant contributions to AI globally. Stanford University’s AI Index, which assesses AI advancements worldwide across various metrics in research, development, and economy, ranks China among the top three countries for global AI vibrancy. 1 On research, for example, China produced about one-third of both AI journal papers and AI citations worldwide in 2021. In economic investment, China accounted for nearly one-fifth of global private investment funding in 2021, attracting USD17 billion for AI start-ups. 2

Five types of AI companies in China

In China, we find that AI companies typically fall into one of five main categories:

  • Hyperscalers develop end-to-end AI technology capability and collaborate within the ecosystem to serve both business-to-business and business-to-consumer companies.
  • Traditional industry companies serve customers directly by developing and adopting AI in internal transformation, new-product launch, and customer services.
  • Vertical-specific AI companies develop software and solutions for specific domain use cases.
  • AI core tech providers provide access to computer vision, natural-language processing, voice recognition, and machine learning capabilities to develop AI systems.
  • Hardware companies provide the hardware infrastructure to support AI demand in computing power and storage.

Today, AI adoption is high in China in finance, retail, and high tech, which together account for more than one-third of the country’s AI market (see sidebar “Five types of AI companies in China”). 3 In tech, for example, leaders Alibaba and ByteDance, both household names in China, have become known for their highly personalized AI-driven consumer apps. In fact, most of the AI applications that have been widely adopted in China to date have been in consumer-facing industries, propelled by the world’s largest internet consumer base and the ability to engage with consumers in new ways to increase customer loyalty, revenue, and market valuations.

So what’s next for AI in China?

In the coming decade, our research indicates that there is tremendous opportunity for AI growth in new sectors in China, including some where innovation and R&D spending have traditionally lagged global counterparts: automotive, transportation, and logistics; manufacturing; enterprise software; and healthcare and life sciences. (See sidebar “About the research.”) In these sectors, we see clusters of use cases where AI can create upwards of USD600 billion in economic value annually. (To provide a sense of scale, the 2021 gross domestic product in Shanghai, China’s most populous city of nearly 28 million, was roughly USD680 billion.) In some cases, this value will come from revenue generated by AI-enabled offerings, while in other cases, it will be generated by cost savings through greater efficiency and productivity. These clusters are likely to become battlegrounds for companies in each sector that will help define the market leaders.

Unlocking the full potential of these AI opportunities typically requires significant investments—in some cases, much more than leaders might expect—on multiple fronts, including the data and technologies that will underpin AI systems, the right talent and organisational mindsets to build these systems, and new business models and partnerships to create data ecosystems, industry standards, and regulations. In our work and global research, we find many of these enablers are becoming standard practice among companies getting the most value from AI.

To help leaders and investors marshal their resources to accelerate, disrupt, and lead in AI, we dive into the research, first sharing where the biggest opportunities lie in each sector and then outlining the core enablers to be tackled first.

Following the money to the most promising sectors

We looked at the AI market in China to determine where AI could deliver the most value in the future. We studied market projections at length and dug deep into country and segment-level reports worldwide to see where AI was delivering the greatest value across the global landscape. We then spoke in depth with experts across sectors in China to understand where the greatest opportunities could emerge next. Our research led us to several sectors: automotive, transportation, and logistics, which are collectively expected to contribute the majority—around 64 % – of the USD 600 billion opportunity; manufacturing, which will drive another 19 %; enterprise software, contributing 13 %; and healthcare and life sciences, at 4 % of the opportunity.

Within each sector, our analysis shows the value-creation opportunity concentrated within only two to three domains. These are typically in areas where private-equity and venture-capital-firm investments have been high in the past five years and successful proof of concepts have been delivered.

Automotive, transportation, and logistics

China’s auto market stands as the largest in the world, with the number of vehicles in use surpassing that of the United States. The sheer size—which we estimate to grow to more than 300 million passenger vehicles on the road in China by 2030—provides a fertile landscape of AI opportunities. Indeed, our research finds that AI could have the greatest potential impact on this sector, delivering more than USD380 billion in economic value. This value creation will likely be generated predominantly in three areas: autonomous vehicles, personalization for auto owners, and fleet asset management.

Autonomous, or self-driving, vehicles. Autonomous vehicles make up the largest portion of value creation in this sector (USD335 billion). Some of this new value is expected to come from a reduction in financial losses, such as medical, first-responder, and vehicle costs. Roadway accidents stand to decrease an estimated 3 to 5 % annually as autonomous vehicles actively navigate their surroundings and make real-time driving decisions without being subject to the many distractions, such as text messaging, that tempt humans. Value would also come from savings realized by drivers as cities and enterprises replace passenger vans and buses with shared autonomous vehicles. 4

Already, significant progress has been made by both traditional automotive OEMs and AI players to advance autonomous-driving capabilities to level 4 (where the driver doesn’t need to pay attention but can take over controls) and level 5 (fully autonomous capabilities in which inclusion of a steering wheel is optional). For instance, WeRide, which achieved level 4 autonomous-driving capabilities, 5 completed a pilot of its Robotaxi in Guangzhou, with nearly 150,000 trips in one year without any accidents with active liability. 6

Personalised experiences for car owners. By using AI to analyze sensor and GPS data—including vehicle-parts conditions, fuel consumption, route selection, and steering habits—car manufacturers and AI players can increasingly tailor recommendations for hardware and software updates and personalize car owners’ driving experience. Automaker NIO’s advanced driver-assistance system and battery-management system, for instance, can track the health of electric-car batteries in real time, diagnose usage patterns, and optimize charging cadence to improve battery life span while drivers go about their day. Our research finds this could deliver USD30 billion in economic value by reducing maintenance costs and unanticipated vehicle failures, as well as generating incremental revenue for companies that identify ways to monetize software updates and new capabilities. 7

Fleet asset management. AI could also prove critical in helping fleet managers better navigate China’s immense network of railway, highway, inland waterway, and civil aviation routes, which are some of the longest in the world. Our research finds that USD15 billion in value creation could emerge as OEMs and AI players specializing in logistics develop operations research optimizers that can analyze IoT data and identify more fuel-efficient routes and lower-cost maintenance stops for fleet operators. 8 One automotive OEM in China now offers fleet owners and operators an AI-driven management system for monitoring fleet locations, tracking fleet conditions, and analyzing trips and routes. It is estimated to save up to 15 % in fuel and maintenance costs.


In manufacturing, China is evolving its reputation from a low-cost manufacturing hub for toys and clothes to a leader in precision manufacturing for processors, chips, engines, and other high-end components. Our findings show AI can help facilitate this shift from manufacturing execution to manufacturing innovation and create USD 115 billion in economic value.

The majority of this value creation (USD 100 billion) will likely come from innovations in process design through the use of various AI applications, such as collaborative robotics that create the next-generation assembly line, and digital twins that replicate real-world assets for use in simulation and optimization engines. 9 With digital twins, manufacturers, machinery and robotics providers, and system automation providers can simulate, test, and validate manufacturing-process outcomes, such as product yield or production-line productivity, before commencing large-scale production so they can identify costly process inefficiencies early. One local electronics manufacturer uses wearable sensors to capture and digitize hand and body movements of workers to model human performance on its production line. It then optimizes equipment parameters and setups—for example, by changing the angle of each workstation based on the worker’s height—to reduce the likelihood of worker injuries while improving worker comfort and productivity.

The remainder of value creation in this sector (USD 15 billion) is expected to come from AI-driven improvements in product development. 10 Companies could use digital twins to rapidly test and validate new product designs to reduce R&D costs, improve product quality, and drive new product innovation. On the global stage, Google has offered a glimpse of what’s possible: it has used AI to rapidly assess how different component layouts will alter a chip’s power consumption, performance metrics, and size. This approach can yield an optimal chip design in a fraction of the time design engineers would take alone.

Enterprise software

As in other countries, companies based in China are undergoing digital and AI transformations, leading to the emergence of new local enterprise-software industries to support the necessary technological foundations.

Solutions delivered by these companies are estimated to deliver another USD80 billion in economic value. Offerings for cloud and AI tooling are expected to provide more than half of this value creation (USD45 billion). 11 In one case, a local cloud provider serves more than 100 local banks and insurance companies in China with an integrated data platform that enables them to operate across both cloud and on-premises environments and reduces the cost of database development and storage. In another case, an AI tool provider in China has developed a shared AI algorithm platform that can help its data scientists automatically train, predict, and update the model for a given prediction problem. Using the shared platform has reduced model production time from three months to about two weeks.

AI-driven software-as-a-service (SaaS) applications are expected to contribute the remaining USD 35 billion in economic value in this category. 12 Local SaaS application developers can apply multiple AI techniques (for instance, computer vision, natural-language processing, machine learning) to help companies make predictions and decisions across enterprise functions in finance and tax, human resources, supply chain, and cybersecurity. A leading financial institution in China has deployed a local AI-driven SaaS solution that uses AI bots to offer personalized training recommendations to employees based on their career path.

Healthcare and life sciences

In recent years, China has stepped up its investment in innovation in healthcare and life sciences with AI. China’s “14th Five-Year Plan” targets 7 % annual growth by 2025 for R&D expenditure, of which at least 8 % is devoted to basic research. 13

One area of focus is accelerating drug discovery and increasing the odds of success, which is a significant global issue. In 2021, global pharma R&D spend reached USD212 billion, compared with USD137 billion in 2012, with an approximately 5 % compound annual growth rate (CAGR). Drug discovery takes 5.5 years on average, which not only delays patients’ access to innovative therapeutics but also shortens the patent protection period that rewards innovation. Despite improved success rates for new-drug development, only the top 20 % of pharmaceutical companies worldwide realized a breakeven on their R&D investments after seven years.

Another top priority is improving patient care, and Chinese AI start-ups today are working to build the country’s reputation for providing more accurate and reliable healthcare in terms of diagnostic outcomes and clinical decisions.

Our research suggests that AI in R&D could add more than USD25 billion in economic value in three specific areas: faster drug discovery, clinical-trial optimization, and clinical-decision support.

Rapid drug discovery. Novel drugs (patented prescription drugs) currently account for less than 30 % of the total market size in China (compared with more than 70 % globally), indicating a significant opportunity from introducing novel drugs empowered by AI in discovery. We estimate that using AI to accelerate target identification and novel molecules design could contribute up to USD10 billion in value. 14 Already more than 20 AI start-ups in China funded by private-equity firms or local hyperscalers are collaborating with traditional pharmaceutical companies or independently working to develop novel therapeutics. Insilico Medicine, by using an end-to-end generative AI engine for target identification, molecule design, and lead optimization, discovered a preclinical candidate for pulmonary fibrosis in less than 18 months at a cost of under USD3 million. This represented a significant reduction from the average timeline of six years and an average cost of more than USD18 million from target discovery to preclinical candidate. This antifibrotic drug candidate has now successfully completed a Phase 0 clinical study and entered a Phase I clinical trial.

Clinical-trial optimisation. Our research suggests that another USD10 billion in economic value could result from optimizing clinical-study designs (process, protocols, sites), optimising trial delivery and execution (hybrid trial-delivery model), and generating real-world evidence. 15 These AI use cases can reduce the time and cost of clinical-trial development, provide a better experience for patients and healthcare professionals, and enable higher quality and compliance. For instance, a global top 20 pharmaceutical company leveraged AI in combination with process improvements to reduce the clinical-trial enrollment timeline by 13 % and save 10 to 15 % in external costs. The global pharmaceutical company prioritized three areas for its tech-enabled clinical-trial development. To accelerate trial design and operational planning, it utilized the power of both internal and external data for optimizing protocol design and site selection. For streamlining site and patient engagement, it established an ecosystem with API standards to leverage internal and external innovations. To establish a clinical-trial development cockpit, it aggregated and visualized operational trial data to enable end-to-end clinical-trial operations with full transparency so it could predict potential risks and trial delays and proactively take action.

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Clinical-decision support. Our findings indicate that the use of machine learning algorithms on medical images and data (including examination results and symptom reports) to predict diagnostic outcomes and support clinical decisions could generate around USD5 billion in economic value. 16 A leading AI start-up in medical imaging now applies computer vision and machine learning algorithms on optical coherence tomography results from retinal images. It automatically searches and identifies the signs of dozens of chronic illnesses and conditions, such as diabetes, hypertension, and arteriosclerosis, expediting the diagnosis process and increasing early detection of disease.

How to unlock these opportunities

During our research, we found that realizing the value from AI would require every sector to drive significant investment and innovation across six key enabling areas (exhibit). The first four areas are data, talent, technology, and significant work to shift mindsets as part of adoption and scaling efforts. The remaining two, ecosystem orchestration and navigating regulations, can be considered collectively as market collaboration and should be addressed as part of strategy efforts.

Some specific challenges in these areas are unique to each sector. For example, in automotive, transportation, and logistics, keeping pace with the latest advances in 5G and connected-vehicle technologies (commonly referred to as V2X) is crucial to unlocking the value in that sector. Those in healthcare will want to stay current on advances in AI explainability; for providers and patients to trust the AI, they must be able to understand why an algorithm made the decision or recommendation it did.

Broadly speaking, four of these areas—data, talent, technology, and market collaboration—stood out as common challenges that we believe will have an outsized impact on the economic value achieved. Without them, tackling the others will be much harder.


For AI systems to work properly, they need access to high-quality data, meaning the data must be available, usable, reliable, relevant, and secure. This can be challenging without the right foundations for storing, processing, and managing the vast volumes of data being generated today. In the automotive sector, for instance, the ability to process and support up to two terabytes of data per car and road data daily is necessary for enabling autonomous vehicles to understand what’s ahead and delivering personalized experiences to human drivers. In healthcare, AI models need to take in vast amounts of omics 17 data to understand diseases, identify new targets, and design new molecules.

Companies seeing the highest returns from AI—more than 20 percent of earnings before interest and taxes (EBIT) contributed by AI—offer some insights into what it takes to achieve this. McKinsey’s 2021 Global AI Survey shows that these high performers are much more likely to invest in core data practices, such as rapidly integrating internal structured data for use in AI systems (51 % of high performers versus 32 % of other companies), establishing a data dictionary that is accessible across their enterprise (53 % versus 29 %), and developing well-defined processes for data governance (45 % versus 37 %).

Participation in data sharing and data ecosystems is also crucial, as these partnerships can lead to insights that would not be possible otherwise. For instance, medical big data and AI companies are now partnering with a wide range of hospitals and research institutes, integrating their electronic medical records (EMR) with publicly available medical-research data and clinical-trial data from pharmaceutical companies or contract research organizations. The goal is to facilitate drug discovery, clinical trials, and decision making at the point of care so providers can better identify the right treatment procedures and plan for each patient, thus increasing treatment effectiveness and reducing chances of adverse side effects. One such company, Yidu Cloud, has provided big data platforms and solutions to more than 500 hospitals in China and has, upon authorization, analyzed more than 1.3 billion healthcare records since 2017 for use in real-world disease models to support a variety of use cases including clinical research, hospital management, and policy making.

For AI systems to work properly, they need access to high-quality data, meaning the data must be available, usable, reliable, relevant, and secure. This can be challenging without the right foundations for storing, processing, and managing the vast volumes of data being generated today. In the automotive sector, for instance, the ability to process and support up to two terabytes of data per car and road data daily is necessary for enabling autonomous vehicles to understand what’s ahead and delivering personalized experiences to human drivers. In healthcare, AI models need to take in vast amounts of omics 17 data to understand diseases, identify new targets, and design new molecules.

Companies seeing the highest returns from AI—more than 20 % of earnings before interest and taxes (EBIT) contributed by AI—offer some insights into what it takes to achieve this. McKinsey’s 2021 Global AI Survey shows that these high performers are much more likely to invest in core data practices, such as rapidly integrating internal structured data for use in AI systems (51 % of high performers versus 32 % of other companies), establishing a data dictionary that is accessible across their enterprise (53 % versus 29 %), and developing well-defined processes for data governance (45 % versus 37 %).

Participation in data sharing and data ecosystems is also crucial, as these partnerships can lead to insights that would not be possible otherwise. For instance, medical big data and AI companies are now partnering with a wide range of hospitals and research institutes, integrating their electronic medical records (EMR) with publicly available medical-research data and clinical-trial data from pharmaceutical companies or contract research organizations. The goal is to facilitate drug discovery, clinical trials, and decision making at the point of care so providers can better identify the right treatment procedures and plan for each patient, thus increasing treatment effectiveness and reducing chances of adverse side effects. One such company, Yidu Cloud, has provided big data platforms and solutions to more than 500 hospitals in China and has, upon authorization, analyzed more than 1.3 billion healthcare records since 2017 for use in real-world disease models to support a variety of use cases including clinical research, hospital management, and policy making.


In our experience, we find it nearly impossible for businesses to deliver impact with AI without business domain knowledge. Knowing what questions to ask in each domain can determine the success or failure of a given AI effort. As a result, organisations in all four sectors (automotive, transportation, and logistics; manufacturing; enterprise software; and healthcare and life sciences) can benefit from systematically upskilling existing AI experts and knowledge workers to become AI translators—individuals who know what business questions to ask and can translate business problems into AI solutions. We like to think of their skills as resembling the Greek letter pi (π). This group has not only a broad mastery of general management skills (the horizontal bar) but also spikes of deep functional knowledge in AI and domain expertise (the vertical bars).

To build this talent profile, some companies upskill technical talent with the requisite skills. One AI start-up in drug discovery, for instance, has created a program to train newly hired data scientists and AI engineers in pharmaceutical domain knowledge such as molecule structure and characteristics. Company executives credit this deep domain knowledge among its AI experts with enabling the discovery of nearly 30 molecules for clinical trials. Other companies seek to arm existing domain talent with the AI skills they need. An electronics manufacturer has built a digital and AI academy to provide on-the-job training to more than 400 employees across different functional areas so that they can lead various digital and AI projects across the enterprise.

Technology maturity

McKinsey has found through past research that having the right technology foundation is a critical driver for AI success. For business leaders in China, our findings highlight four priorities in this area:

Increasing digital adoption. There is room across industries to increase digital adoption. In hospitals and other care providers, many workflows related to patients, personnel, and equipment have yet to be digitized. Further digital adoption is required to provide healthcare organizations with the necessary data for predicting a patient’s eligibility for a clinical trial or providing a physician with intelligent clinical-decision-support tools.

The same holds true in manufacturing, where digitization of factories is low. Implementing IoT sensors across manufacturing equipment and production lines can enable companies to accumulate the data necessary for powering digital twins.

Implementing data science tooling and platforms. The cost of algorithmic development can be high, and companies can benefit greatly from using technology platforms and tooling that streamline model deployment and maintenance, just as they benefit from investments in technologies to improve the efficiency of a factory production line. Some essential capabilities we recommend companies consider include reusable data structures, scalable computation power, and automated MLOps capabilities. All of these contribute to ensuring AI teams can work efficiently and productively.

Advancing cloud infrastructures. Our research finds that while the % of IT workloads on cloud in China is almost on par with global survey numbers, the share on private cloud is much bigger due to security and data compliance concerns. As SaaS vendors and other enterprise-software providers enter this market, we advise that they continue to advance their infrastructures to address these concerns and provide enterprises with a clear value proposition. This will require further advances in virtualization, data-storage capacity, performance, elasticity and resilience, and technological agility to customize business capabilities, which enterprises have come to expect from their vendors.

Investments in AI research and advanced AI techniques. Many of the use cases described here will require fundamental advances in the underlying technologies and techniques. For instance, in manufacturing, additional research is needed to improve the performance of camera sensors and computer vision algorithms to detect and recognize objects in dimly lit environments, which can be common on factory floors. In life sciences, further innovation in wearable devices and AI algorithms is necessary to enable the collection, processing, and integration of real-world data in drug discovery, clinical trials, and clinical-decision-support processes. In automotive, advances for improving self-driving model accuracy and reducing modeling complexity are required to enhance how autonomous vehicles perceive objects and perform in complex scenarios.

For conducting such research, academic collaborations between enterprises and universities can advance what’s possible.

Market collaboration

AI can present challenges that transcend the capabilities of any one company, which often gives rise to regulations and partnerships that can further AI innovation. In many markets globally, we’ve seen new regulations, such as Global Data Protection Regulation (GDPR) in Europe and the California Consumer Privacy Act in the United States, begin to address emerging issues such as data privacy, which is considered a top AI relevant risk in our 2021 Global AI Survey. And proposed European Union regulations designed to address the development and use of AI more broadly will have implications globally.

Our research points to three areas where additional efforts could help China unlock the full economic value of AI:

Data privacy and sharing. For individuals to share their data, whether it’s healthcare or driving data, they need to have an easy way to give permission to use their data and have trust that it will be used appropriately by authorized entities and safely shared and stored. Guidelines related to privacy and sharing can create more confidence and thus enable greater AI adoption. A 2019 law enacted in China to improve citizen health, for instance, promotes the use of big data and AI by developing technical standards on the collection, storage, analysis, and application of medical and health data. 18

Meanwhile, there has been significant momentum in industry and academia to build methods and frameworks to help mitigate privacy concerns. For example, the number of papers mentioning “privacy” accepted by the Neural Information Processing Systems, a leading machine learning conference, has increased sixfold in the past five years. 19

Market alignment. In some cases, new business models enabled by AI will raise fundamental questions around the use and delivery of AI among the various stakeholders. In healthcare, for instance, as companies develop new AI systems for clinical-decision support, debate will likely emerge among government and healthcare providers and payers as to when AI is effective in improving diagnosis and treatment recommendations and how providers will be reimbursed when using such systems. In transportation and logistics, issues around how government and insurers determine culpability have already arisen in China following accidents involving both autonomous vehicles and vehicles operated by humans. Settlements in these accidents have created precedents to guide future decisions, but further codification can help ensure consistency and clarity.

Standard processes and protocols. Standards enable the sharing of data within and across ecosystems. In the healthcare and life sciences sectors, academic medical research, clinical-trial data, and patient medical data need to be well structured and documented in a uniform manner to accelerate drug discovery and clinical trials. A push by the National Health Commission in China to build a data foundation for EMRs and disease databases in 2018 has led to some movement here with the creation of a standardized disease database and EMRs for use in AI. However, standards and protocols around how the data are structured, processed, and connected can be beneficial for further use of the raw-data records.

Likewise, standards can also eliminate process delays that can derail innovation and scare off investors and talent. An example involves the acceleration of drug discovery using real-world evidence in Hainan’s medical tourism zone; translating that success into transparent approval protocols can help ensure consistent licensing across the country and ultimately would build trust in new discoveries. On the manufacturing side, standards for how organizations label the various features of an object (such as the size and shape of a part or the end product) on the production line can make it easier for companies to leverage algorithms from one factory to another, without having to undergo costly retraining efforts.

Patent protections. Traditionally, in China, new innovations are rapidly folded into the public domain, making it difficult for enterprise-software and AI players to realize a return on their sizable investment. In our experience, patent laws that protect intellectual property can increase investors’ confidence and attract more investment in this area.

AI has the potential to reshape key sectors in China. However, among business domains in these sectors with the most valuable use cases, there is no low-hanging fruit where AI can be implemented with little additional investment. Rather, our research finds that unlocking maximum potential of this opportunity will be possible only with strategic investments and innovations across several dimensions—with data, talent, technology, and market collaboration being foremost. Working together, enterprises, AI players, and government can address these conditions and enable China to capture the full value at stake.


This will be the beginning of the second item:

Retail Retreats

‘Meet Me by the Fountain’ Review: Enclosed Encounters

Frank Gehry and Cesar Pelli designed them. Jane Jacobs admired them. The mall isn’t just a hangout for teenagers.

By guest author Alex Beam from the Wall Street Journal, Mr. Beam’s latest book is “Broken Glass: Mies van der Rohe, Edith Farnsworth, and the Fight Over a Modernist Masterpiece.”

Ah, the mall. Where teens mooch away afternoons savoring their content-free existence. Where old folks exercise-walk at a tortoise-like pace, and shoppers struggle, in vain, to resist the blandishments of tables piled high with discounted sweaters. It is hard to imagine a less-promising subject of inquiry than the shopping mall, through which one moves, as Joan Didion once wrote, “in an aqueous suspension not only of light but of judgment.”

The architecture and design writer Alexandra Lange agrees. In her introduction to “Meet Me by the Fountain: An Inside History of the Mall,” she considers the all-too-familiar retail and “lifestyle centers” to be “ubiquitous and underexamined and potentially a little bit embarrassing as the object of serious study.” She then proceeds to examine them, thoroughly, seriously and in an engaging fashion.

If you regard a trip to the mall as a forced march through one of the lower circles of Hell, then Ms. Lange is your perfect Virgil. She was a Mall Girl. She grew up in North Carolina, she tells us, where the Northgate Mall in Durham gave her “somewhere to go.” Her tastes evolved from Sears to the Gap to the bookstore B. Dalton, as she met up “with friends at the multiplex” and fretted over “whether we would be allowed into R-rated movies.” For Ms. Lange and millions like her, the mall was where adolescents tried on “grown-up behavior.”

Ms. Lange takes malls seriously—and look who else does: Frank Gehry, who designed Santa Monica Place, and Cesar Pelli, who designed the Commons in Columbus, Ind. One critic likened I.M. Pei’s 1978 East Building addition to the National Gallery in Washington, D.C., to “the poshest of suburban shopping malls,” not intended as a compliment. “Pei, who had a long career in commercial design,” Ms. Lange notes, “would likely have had no issue with the comparison.”

Even Jane Jacobs, the patron saint of “livable” cities, celebrated Victor Gruen’s new Northland Center out-side Detroit in 1954, calling it “the first modern pedestrian commercial center to use an urban ‘market town’ plan, a compact form physically and psychologically suited to pedestrian shopping.”

Of course malls can’t please everyone. “Who wants to sit in that desolate looking spot?” Frank Lloyd Wright exclaimed when asked to comment on the Gruen-designed Southdale—America’s first enclosed mall—which opened in 1956 near Minneapolis. “You have tried to bring downtown out here. You should have left downtown downtown.”

It was not to be. Ms. Lange artfully elucidates the 70-year history of the mall, beginning with Northland Center, anchored by a J.L. Hudson’s department store. The mall featured contemporary sculptures and a 22-foot-tall wood-and-steel totem pole that became a community meeting spot, like the fountain in the title of Ms. Lange’s book. Northland spawned the concept of the “Gruen transfer,” which the author explains as “the moment when your presence at the mall tips from being goal-oriented . . . into a pleasure in itself.”

Ms. Lange shows that the idea of changing shopping from a chore into a pleasure dates back to the 1869 opening of Paris’s Bon Marché department store, the subject of Émile Zola’s 1883 novel, “The Ladies’ Paradise.” Bon Marché and its ilk, Ms. Lange explains, “transformed the practical task of provisioning a family from a neighborhood round to a leisure pursuit.” In her 1975 essay “On the Mall,” Didion notes that she came to the open-air Ala Moana Center in Honolulu intending to buy a New York Times, yet emerged with two straw hats, four bottles of nail polish and a toaster. Hold the presses! Joan Didion is having fun!

Five years after Didion’s Ala Moana visit, Ms. Lange writes, the mall concept was showing its age: “The first generation of mallgoers was old enough to have children,” and the media “raised the specter of the death of the mall for the first time.” Cue the appearance of Mall 2.0, the successor generation of shopping malls that transformed the property from “a place of consumption” to “a place of experience.” An important stage in the mall’s evolution was developer James Rouse’s first “festival marketplace,” the revivified Faneuil Hall market in Boston. “Downtown still had life in it,” Ms. Lange writes. “The easiest way to think of Faneuil Hall is as a shopping mall whose anchor stores are Boston City Hall and the waterfront.”

The next-generation mall would embody village concepts, perhaps the most famous being the Mall of America, which opened in Bloomington, Minn., in 1992. Ms. Lange describes the Mall of America as “a perpetual carnival,” including the seven-acre Camp Snoopy theme park with its roller coasters, carousels and windmills.

Another example is Los Angeles’s Universal CityWalk, which purports to be a miniature city complete with theme park, cinema multiplex, plenty of stores and a pop-up King Kong to frighten and delight visitors. “This isn’t the L.A. we did get,” CityWalk designer Jon Jerde told Los Angeles magazine, “but it’s the L.A. we could have gotten—the quintessential, idealized L.A.”

The jarring takeaway: City bad, mall good. Mother Jones magazine ungraciously noted that MCA Universal executives “have always stressed the most popular parts of L.A. are now too dangerous; the city scene is a criminalized Third World. So now they go off the real world to simulators.”

A particular strength of Ms. Lange’s book is her canny appreciation of the mall’s resilience. She asserts that malls, as “blank boxes in the middle of the big empty parking lots,” can “serve as a land trust” for the 21st century. This sounds like a stretch, but it proves to be true. Some malls die, but most don’t: In 2017, Credit Suisse predicted that about one-fifth to one-quarter of America’s 1,100 malls would go out of business by 2022. Postpandemic, Ms. Lange tells us, the predicted death rate rose to one-third.

Ms. Lange does run on a bit about mall zombies and “mallwave” music—“like half listening to music in an elevator or a doctor’s office”—but she convincingly argues that deceased malls can be a resource: “Where else could a city, or a suburb, ‘find’ over one hundred thousand square feet of available space for a new public library?” The retail concourses at the Northland Center mall are now being converted into residential lofts. A developer plans to convert a portion of the 8,000-car parking lot into middle-income rental housing integrated with shops and restaurants. The new project will be rechristened Northland City Center. Somewhere, Jane Jacobs is smiling.

In a chapter on the “postapocalyptic mall,” Ms. Lange introduces us to the Renzo Piano-designed City Center Bishop Ranch in San Ramon, Calif. The 300,000-square-foot City Center has a dine-in movie theatre, a gym, and a panoply of shops and restaurants. Yet in a promotional video, Mr. Piano says: “I just want to say, this is not a shopping mall; it is something completely different.” Ms. Lange’s elegant conclusion: The mall is dead; long live the mall.


Here beginns the third feature:

How AI Could Help Predict—and Avoid—Sports Injuries, Boost Performance

Computer vision, the technology behind facial recognition, will change the game in real-time analysis of athletes and sharpen training prescriptions, analytics experts say.

By guest author Eric Niiler from the New York Times

Imagine a stadium where ultra-high-resolution video feeds and camera-carrying drones track how individual players’ joints flex during a game, how high they jump or fast they run—and, using AI, precisely identify athletes’ risk of injury in real time.

Coaches and elite athletes are betting on new technologies that combine artificial intelligence with video to predict injuries before they happen and provide highly tailored prescriptions for workouts and practice drills to reduce the risk of getting hurt. In coming years, computer-vision technologies similar to those used in facial-recognition systems at airport checkpoints will take such analysis to a new level, making the wearable sensors in wide use by athletes today unnecessary, sports-analytics experts predict.

A look at how innovation and technology are transforming the way we live, work and play.

This data revolution will mean that some overuse injuries may be greatly reduced in the future, says Stephen Smith, CEO and founder of Kitman Labs, a data firm working in several pro sports leagues with offices in Silicon Valley and Dublin. “There are athletes that are treating their body like a business, and they’ve started to leverage data and information to better manage themselves,” he says. “We will see way more athletes playing far longer and playing at the highest level far longer as well.”

A baseball app called Mustard is among those that already employ computer vision. Videos recorded and submitted by users are compared to a database of professional pitchers’ moves, guiding the app to suggest prescriptive drills aimed to help throw more efficiently. Mustard, which comes in a version that is free to download, is designed to help aspiring ballplayers improve their performance, as well as avoiding the kind of repetitive motions that can cause long-term pain and injury, according to CEO and co-founder Rocky Collis.

Computer vision is also making inroads in apps for other sports, like golf, and promises to have relevance for amateurs as well as pros in the future. In wider use now are algorithms using a form of AI known as machine learning that crunches statistical data from sensors and can analyze changes in body position or movement that could indicate fatigue, weaknesses or a potential injury. Liverpool Football Club in the U.K. says it reduced the number of injuries to its players by a third over last season after adopting an AI-based data-analytics program from the company Zone7. The information is used to tailor prescriptions for training and suggest optimal time to rest.

Soccer has been among the biggest adopters of AI-driven data analytics as teams look for any kind of edge in the global sport. But some individual sports are also beginning to use these technologies. At the 2022 Winter Olympics in Beijing, ten U.S. figure skaters used a system called 4D Motion, developed by New Jersey-based firm 4D Motion Sports, to help track fatigue that can be the result of taking too many jumps in practice, says Lindsay Slater, sports sciences manager for U.S. Figure Skating and an assistant professor of physical therapy at the University of Illinois Chicago. Skaters strapped a small device to the hip and then reviewed the movement data with their coach when practice was done.

“We’ve actually gotten the algorithm to the point where we can really define the takeoff and landing of a jump, and we can estimate that the stresses at the hip and the trunk are quite high,” Dr. Slater says. “Over the course of the day, we found that the athletes have reduced angular velocity, reduced jump height, they’re cheating more jumps, which is where those chronic and overuse injuries tend to happen.” She says U.S. Figure Skating is assessing the 4D system in a pilot project before expanding its use to more of its athletes.

Algorithms still have many hurdles to overcome in predicting the risk of an injury. For one, it’s difficult to collect long-term data from athletes who jump from team to team every few years. Also, data collected by sensors can vary slightly depending on the manufacturer of the device, while visual data has an advantage of being collected remotely, without the worry that a sensor might fail, analytics experts say.

Psychological and emotional factors that affect performance can’t easily be measured: stress during contract talks, a fight with a spouse, bad food the night before. And the only way to truly test the algorithms is to see if a player who has been flagged as a risk by an AI program actually gets hurt in a game–a test that would violate ethical rules, says Devin Pleuler, director of analytics at Toronto FC, one of 28 teams in Major League Soccer.

“I do think that there might be a future where these things can be trusted and reliable,” Mr. Pleuler says. “But I think that there are significant sample-size issues and ethical issues that we need to overcome before we really reach that sort of threshold.”

Also presenting challenges are data-privacy issues and the question of whether individual athletes should be compensated when teams collect their information to feed AI algorithms.

The U.S. currently has no regulations that prohibit companies from capturing and using player training data, according to Adam Solander, a Washington, D.C., attorney who represents several major sports teams and data-analytics firms. He notes the White House is developing recommendations on rules governing artificial intelligence and the use of private data.

Those regulations will need to strike a balance in order to allow potentially important technologies to help people, while still taking privacy rights of individuals into consideration, Mr. Solander says.

For now, one sports-data firm that has adopted computer vision is using it not to predict injuries, but to predict the next superstar. Paris-based SkillCorner collects broadcast television video from 45 soccer leagues around the world and runs it through an algorithm that tracks individual players’ location and speed, says Paul Neilson, the company’s general manager.

The firm’s 65 clients now use the data to scout potential recruits, but Mr. Neilson expects that in the near future the company’s game video might be used in efforts to identify injuries before they occur. Yet he doubts an AI algorithm will ever replace a human coach on the sideline.

“During a game, you are right there and you can smell it, feel it, touch it almost,” he says. “For these decision makers, I think it’s still less likely that they will actually listen to an insight that’s coming from an artificial-intelligence source.”

Appeared in the June 9, 2022, print edition as ‘HOW AI WILL PREDICT— AND AVOID—INJURIES.





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