[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-83449-en":3,"doc-seo-83449-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},83449,1099513958607,"Jiven","https://ap-avatar.wpscdn.com/avatar/100002390cf8733938c?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778829742770036399",8,"Research & Report","Data Sharing and Competition in Learning-by-Deploying Industries","Deployment-based technologies generate feedback data that improves future performance, making pooled learning across deployments a key driver of productivity growth. This study examines when pooled learning is socially valuable, when firms sustain data sharing voluntarily, and how product-market competition reshapes the equilibrium. A two-period model contrasts pooled versus fragmented learning-by-deploying, and characterizes a sustainability threshold tied to learning persistence and demand elasticity. Numerical analysis under general inverse demand confirms robustness.","arXiv :2607 .00168v1 [ cs .GT] 30 Jun 2026  \nData Sharing and Competition in Learning-by-Deploying Industries  \nInsights from Robotics and Beyond  \nYunjin Tong  \nStanford Graduate School of Business  \nLuca-Andrei Manea Stanford Graduate School of Business  \n1 Introduction  \nMany modern technologies improve through use. Each unit deployed in the field produces output and, at the same time, generates data that is fed back to train and refine the technology itself, so that the next generation of units is more productive. Autonomous vehicles, logistics systems, predictive maintenance, industrial inspection, and robots all share this feature: deployment is not only production but also an investment in a shared, cumulative learning stock. When that data is pooled across many deployments, the productivity gains spill over to the entire fleet. When it is siloed, each operator learns only from its own history. The architecture of learning, pooled versus fragmented, therefore shapes how fast the technology improves and how the gains are distributed. Robots are the most current example. Companies such as Agility Robotics, Figure, Unitree, and Tesla are deploying physical units in warehouses and factories, while the control policies that run them are increasingly trained as foundation models that pool deployment data across an entire fleet. A unit deployed today produces output and generates training data that increases the productivity of every unit tomorrow. This learning structure now sits at the center of an industrial-policy contest. China’s 15th Five-Year Plan (2026–2030) elevates “embodied intelligence” to a top-line national priority, backed by coordinated procurement, dedicated standardization committees, and vertically integrated platforms that consolidate deployment data across many sites. By 2024 China already installed over half of the world’s industrial robots and operated a stock exceeding two million units [The Diplomat, 2026, U.S.-China Economic and Security Review Commission, 2024] . The United States, by contrast, leads in AI software and high-end research but deploys through a more fragmented landscape of competing platforms and decentralized purchasers, with a comparatively piecemeal policy response [U.S.-China Economic and Security Review Commission, 2024] . The conventional framing of this contrast is static, a race over unit costs and supply chains. But the underlying object is dynamic. What matters is how the architecture of learning interacts with firms’ adoption decisions over time and which policy levers shift the resulting equilibrium. We study this with a deliberately simple two-period model. Symmetric firms make irreversible capacity decisions. Capacity in use generates data that feeds a learning curve, which increases next-period productivity. The data may be pooled, with all firms drawing on a common stock generated by joint deployment, or fragmented, with each firm learning only from its own history. We follow the learning-by-doing tradition of Arrow [1962], but replace production experience with deployment data as the source of productivity growth, a distinction we term learning-by-deploying. While robots are one motivating example, the model applies to any learning-by-deploying industry in which use generates feedback data that improves a shared technology.  \nContribution. We isolate when pooled learning is socially valuable, when firms will sustain it voluntarily, and how product-market competition changes both answers. In a baseline with an  \nexogenous output price, pooling is unambiguously beneficial and firms underinvest in early deployment relative to a social planner. Once the price is endogenized through downstream Cournot competition, this clean prescription breaks down. Pooling raises every firm’s output at once, which decreases the price, so the private value of sharing falls with competition intensity and can turn negative. We characterize a sharing-sustainability threshold, show it is govern","cbCaiaR0p9Cz86S8","https://ap.wps.com/l/cbCaiaR0p9Cz86S8","pdf",675363,3,1,20,"English","en",105,"# Introduction\n## Contribution and Policy Implications\n# Related Work","[{\"question\":\"Why does deployment-based learning matter for technology improvement?\",\"answer\":\"Each deployment produces output and generates data that trains and refines the technology, so later units become more productive. When data is pooled, learning spills over across the fleet; when siloed, each operator improves only from its own history.\"},{\"question\":\"What is the paper’s core modeling approach?\",\"answer\":\"The paper uses a simple two-period model with symmetric firms making irreversible capacity choices. Capacity in use generates deployment data that drives a learning curve, improving next-period productivity under pooled or fragmented learning-by-deploying.\"},{\"question\":\"How does product-market competition affect the value of data sharing?\",\"answer\":\"With downstream Cournot competition, pooling increases each firm’s output and depresses prices, reducing the private incentive to share. The paper identifies a sharing-sustainability threshold and shows that competition can make the private value of sharing turn negative.\"}]",1784187990,50,{"code":4,"msg":31,"data":32},"ok",{"site_id":25,"language":24,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"data-sharing-and-competition-in-learning-by-deploying-industries","",{"@graph":36,"@context":85},[37,53,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,50],{"item":41,"name":42,"@type":43,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":20},"https://docshare.wps.com/document/research-report/",{"item":51,"name":13,"@type":43,"position":52},"https://docshare.wps.com/document/data-sharing-and-competition-in-learning-by-deploying-industries/83449/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":24,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-25","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why does deployment-based learning matter for technology improvement?","Question",{"text":75,"@type":76},"Each deployment produces output and generates data that trains and refines the technology, so later units become more productive. When data is pooled, learning spills over across the fleet; when siloed, each operator improves only from its own history.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What is the paper’s core modeling approach?",{"text":80,"@type":76},"The paper uses a simple two-period model with symmetric firms making irreversible capacity choices. Capacity in use generates deployment data that drives a learning curve, improving next-period productivity under pooled or fragmented learning-by-deploying.",{"name":82,"@type":73,"acceptedAnswer":83},"How does product-market competition affect the value of data sharing?",{"text":84,"@type":76},"With downstream Cournot competition, pooling increases each firm’s output and depresses prices, reducing the private incentive to share. The paper identifies a sharing-sustainability threshold and shows that competition can make the private value of sharing turn negative.","https://schema.org",{"og:url":51,"og:type":87,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":89,"canonical":51},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":92},[93,97,101,105,110,114,119,122,126,129,133],{"id":21,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":29,"slug":113},6,"Technology","technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":22,"slug":125},9,"Religion & Spirituality","religion-spirituality",{"id":22,"doc_module":4,"doc_module_name":46,"category_name":127,"show_sort_weight":22,"slug":128},"World Cup","world-cup",{"id":130,"doc_module":4,"doc_module_name":46,"category_name":131,"show_sort_weight":130,"slug":132},10,"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":46,"category_name":135,"show_sort_weight":106,"slug":136},19,"General","general"]