In countries far along in the demographic transition, rapidly aging populations are colliding with a severe shortage of health care and elder care workers. Japan may be the most pertinent case study of this and other impacts of demographic disruption. The country’s fertility rate is decreasing faster than projected, and as of 2025, 30% of its population was aged 65 or older. These trends have contributed to a persistent gap between surging demand for elder care and a shrinking care workforce.

To cope with this crisis, the Japanese government has implemented several programs to promote the use of robots in nursing home care. These care robots (or “kaigo robots”) assist care workers and support residents with daily needs. But how does their adoption affect workflows, staff retention, and overall staffing of long-term care facilities? Few studies have empirically examined these questions.

Shedding light on the debate surrounding new automation technologies and their impact on workers, a recent study, published on August 6 in the inaugural issue of the journal Health Affairs Review, finds that care robots help address challenges posed by population aging and care-labor shortages without necessarily displacing workers or lowering care quality.

The study, titled Robots and Labor in the Service Sector: Evidence from Nursing Homes, is part of a collaborative effort driven by the Asia Health Policy Program (AHPP) at the Walter H. Shorenstein Asia-Pacific Research Center. The study’s co-authors are Stanford health economist Karen Eggleston, the director of AHPP; Toshiaki Iizuka of the University of Tokyo’s Graduate School of Economics, a former AHPP visiting scholar; Yong Suk Lee of the University of Notre Dame’s Keough School of Global Affairs, formerly the SK Center Fellow at Stanford’s Freeman Spogli Institute for International Studies; and Ellie Xi, a rising junior at Stanford studying biomedical computation on the pre-med track and a former research assistant to Dr. Eggleston.

“One of the aspects of the paper that I found most interesting is its attempt to move beyond simply documenting where robots are being adopted and instead ask what their adoption may actually do to the long-term care workforce, especially given the sector’s high turnover rates and physically demanding working conditions,” says Xi.

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Lessons from Early Diffusion of Robotics in the Long-Term Care Sector

The researchers grouped the robots prevalent in Japanese nursing homes into three main categories:

• Transfer aids: Wearable and non-wearable devices that assist staff with lifting frail residents from beds to wheelchairs, significantly reducing back strain, a leading cause of caregiver burnout.
• Mobility robots: Assistive systems that help residents move, use the restroom, or bathe more independently.
• Monitoring and communication systems: Sensors that alert staff when a resident has slipped out of bed or fallen, as well as social robots designed to offer companionship and cognitive stimulation.

To examine early diffusion of these robots, the researchers used 2017 survey data on long-term care work across approximately 860 Japanese nursing homes. A second source of data is their compilation of prefecture-level care robot subsidy availability, using regional variation in robot subsidies as an instrumental variable. Think of this as a natural experiment driven by a “geographic nudge.” Starting in 2015, Japan’s central government began providing prefectural governments with funds to subsidize the cost of adopting care robots. Because different prefectures introduced these subsidies at different times and with varying levels of generosity, nursing homes in some regions had a much easier path to acquiring and deploying robots than those in neighboring areas.

By comparing staffing trends between nursing homes that received this external, subsidy-driven "nudge" to adopt robots and those that did not, the researchers could confidently isolate the technology’s causal effects on the caregiving workforce.

The results indicate that nursing homes that adopted robots saw a significant decrease in staffing retention problems. Robot adoption was also associated with a 28% increase in care workers and a 39% increase in nurses, resulting in approximately 26% higher total employment at the facility level. Importantly, this employment growth occurred entirely among non-regular employees – that is, staff members on flexible, often part-time, contracts. The findings also suggest that robot adoption may improve continuity and quality of care while mitigating careworker shortages.

“Using nationally representative data from Japan and an instrumental-variable strategy, the study provides evidence suggesting that robots can complement rather than simply replace care workers, including by alleviating retention difficulties in a sector facing persistent labor shortages and potentially supporting quality of care,” explains Xi. “The paper’s central finding is that robot adoption is associated with reduced retention difficulties and increased employment of care workers and nurses under flexible contracts.”

A Human-Robot Collaborative Future for Elder Care

Caregiving is a physically demanding and high-stress occupation. The study’s findings indicate that, by introducing robots to ease staff burdens and monitor residents, nursing homes were able to promote more flexible work, either by increasing the tasks performed by non-regular employees or potentially encouraging part-time work. This type of work structure is encouraged by the Japan Nursing Association as a means for reducing stress, preventing burnout, and promoting a healthier work-life balance.

“Given recent debates about AI use in healthcare and the future of work, I found it especially interesting that this research highlights the potential for technology to augment caregivers, particularly by supporting physically demanding or indirect tasks, while preserving the human aspects of care that are more difficult to reproduce,” says Xi.

The study's findings are relevant for policymakers seeking evidence on the social value of collaborative robots and their impacts on staffing and care delivery, particularly as aging societies facing demographic pressures similar to Japan consider how to meet growing demand for long-term care.

A Mentorship Story

The study is part of Eggleston’s Innovation for Healthy Aging research track, which investigates how East Asian economies adopt and adapt to emerging technologies, design incentives, and adjust their workforces to support older adults. It builds on an earlier research project focused on the impact of robots on nursing home care in Japan.

For Xi, co-authoring the paper represents an academic milestone. "As someone deeply interested in geriatrics, I first began working with Dr. Karen Eggleston as a research assistant during the summer after my freshman year," Xi recalls. "Last spring, we decided to continue working together through a directed reading."

During that directed reading, Xi was invited to help revise and update a project that had previously circulated as an Asia Health Policy Program working paper. She worked to update the research, framing the original findings within the rapidly shifting landscape of care robotics and artificial intelligence in long-term care.

"I worked on situating the original findings within more recent research and developments in care robotics and considering what Japan’s experience can tell us about the role of these technologies in aging societies today," Xi says. "It was an incredible opportunity to work closely with Dr. Eggleston, Professor Toshiaki Iizuka, and Professor Yong Suk Lee. I was especially grateful to see firsthand how a collaborative project evolves and to contribute to that process as the paper moved from a working paper toward publication.”

Across the world, populations are aging rapidly as people live longer and fertility rates continue to decline. Asia is at the vanguard of this demographic shift. The number of older adults (aged 60 and above) in the region is projected to triple between 2010 and 2050, reaching nearly 1.3 billion people. As Asian economies face this “silver wave,” helping older adults live safely and independently at home – a concept known as aging in place – has become a policy imperative.

At a recent webinar held during Stanford Health AI Week, the Asia Health Policy Program (AHPP) at Shorenstein APARC brought together experts from China, Singapore, and South Korea to share insights into the potential of health AI to allow older adults to enjoy healthy aging and avoid or postpone institutionalization. 

Moderated by Stanford health economist Karen Eggleston, the director of AHPP, the webinar featured Hongsoo Kim, a professor of health policy and aging at Seoul National University’s Graduate School of Public Health and director of its Artificial Intelligence Institute’s Center for AI in Health and Care; Xiaochen Ma, an assistant professor of health economics at Peking University’s China Center for Health Development Studies; and Tien Yin Wong, a physician-scientist-innovator and the senior vice-chancellor of Tsinghua Medicine and vice-provost of Tsinghua University, who has also worked and held senior leadership roles in Singapore and Australia as a practicing retinal specialist with a research portfolio on retinal diseases, ocular imaging, AI, and digital technology.

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Here are six lessons from the front lines of Asia’s efforts to integrate AI into elderly health care and advance aging in place:

1. Adopt a Whole Systems Approach

In South Korea, the world's fastest-ageing society, automated systems like "CLOVA CareCall" – an AI-powered well-being dialer – conduct natural-sounding check-ins with solo-dwelling seniors, boasting a 96% response rate. Yet, Professor Kim emphasizes that checking in with people in need of health care is only half the battle.

If an AI flags an isolated senior at risk of depression, cognitive decline, or a physical abnormality, but the local community lacks the social workers or clinical pathways to intervene, then the health care system has failed.

“The question is not only whether AI can detect something, but how a health and care system acts on it,” she says. “Detection by itself changes nothing. A warning that no one follows up on helps no one. So the gap I care about is not the model’s cleverness itself. It is whether the system delivers.”

2. Solve the Entire "Care Cascade"

In rural China, traditional diabetic screening rates hover below 33%, leaving millions at risk of Diabetic Retinopathy (DR), a leading cause of blindness. Professor Ma shared how deploying an AI screening model successfully pushed screening rates past 85%.

The research team, however, discovered a glaring bottleneck: only 21% of high-risk patients actually followed up to receive sight-saving treatments. To fill this gap, Ma’s team designed an "AI Plus” model (v2.0) that integrates immediate, local-language counseling at the point of screening. To keep seniors healthy at home, AI solutions must address the entire clinical journey, from initial scan to final treatment.

“Many of the AI tools have been focused on diagnosis accuracy or validation rather than going downstream to the entire cascade of whether improved screening will translate into improved referral and the ultimate health outcomes,” says Ma.

3. Align with Local Workflows and Incentives

AI and other technology solutions for health often fail because they expect overworked care workers to adopt entirely new habits. Professor Ma noted that digital health interventions in rural China succeeded only when they integrated seamlessly into existing daily routines.

Instead of forcing clinicians to use complex new software, successful pilots utilized WeChat, the ubiquitous messaging app already open on every phone. Furthermore, the technology must align with the financial and professional incentives of frontline health workers. If an AI tool increases their administrative burden without simplifying their day or boosting their clinical efficiency, then it will remain unused.

4. Design Human-Centered AI for Health Equity

Professor Wong highlighted the ethical risk that AI tools will worsen, rather than reduce, health care disparities. This challenge is driven by the dynamics of “Inverse Care Law,” where AI disproportionately benefits the already advantaged, and the “Recursive Care Law,” where this inequality becomes a self-reinforcing cycle embedded in the system.

Because younger, more tech-savvy individuals generate more health data, AI models become better at serving them than the intended users of aging-in-place technologies. This creates a vicious cycle where the very tools designed to support aging populations end up marginalizing them. Governments must devise policies to mandate fair data coverage and usability, ensuring that AI serves society's most vulnerable members equitably, Wong stated.

Professor Kim noted that her team found that only about 38% of community care agencies in Korea have adopted AI and that the adoption rate varied sharply by region. In fact, districts with the greatest need may have the least access to these powerful tools. This challenge is a fundamental design gap rather than a technology gap, Professor Kim argues. To be genuinely equitable, a system must be built from the start to actively track who is missing and automatically route support back to them. This requires two  human-centered design key principles:

I. Universal by Default: The hardest-to-reach should not have to be the most persistent in navigating the technology.

II. Connected Across Sectors: Long-term care, social care, and health care must act as one integrated system rather than disconnected silos, each of which sees only part of the person’s needs.

5. Augment, Do Not Replace, the Human Touch

The panelists rejected the trope of robots replacing human caregivers. Instead, they view AI as an essential force multiplier for an overstretched workforce.

Whether it is South Korea’s deployment of 12,000 AI companion robots to combat senior isolation, or automated triage tools in clinics, the goal should be to offload administrative and routine tasks. This frees up human social workers and clinicians to do what they do best: deliver hands-on, empathetic care.

6. Value Real-World Outcomes Over Technical Novelty

Healthcare systems should prioritize rigorous, real-world case studies that prove actual clinical value, such as reduced mortality, lower rates of blindness, or fewer nursing home admissions, rather than celebrating high validation benchmarks in a laboratory.

To build robust future health AI systems, the experts concluded, the academic and tech sectors must also courageously publish and analyze their failed trials to understand what truly works in the chaotic reality of home-based care.

While AI holds immense promise for helping people grow old at home, “age tech” alone cannot solve the elder care crisis, the panelists agreed.

In the Media

Dr. Eggleston joined Jeffrey Snyder, host of the Broadcast Retirement Network, to discuss highlights and takeaways from the webinar discussion. Watch below:

In its June 20, 2026, edition, the Nikkei Shimbun's Market Beat financial analysis column, titled US Stocks Attract Japanese Money, examines how the U.S. stock market is becoming a "major league" that is increasingly attracting capital from Japanese individual investors. Several factors drive this phenomenon:

• Massive IPOs: Unprecedentedly large IPOs, as seen by SpaceX, are capturing global attention and investment.
• Structural Advantages: U.S. markets are moving fast to include new, large companies in major stock indices, which forces index-tracking funds to buy shares and creates automatic demand.
• 24-Hour Trading: U.S. exchanges like the NYSE and Nasdaq are moving toward near 24-hour trading to specifically capture Asian daytime investors, making it easier for them to participate.

The column describes how Japanese investors are increasingly bypassing domestic options to capture the immense growth driven by American AI, data infrastructure, and advanced technology IPOs. "Founders and early-stage investors prefer large-scale markets that offer price discovery, liquidity, and visibility. This is likely to accelerate the concentration of innovative companies in the United States," says Curtis Milhaupt, the William F. Baxter-Visa International Professor of Law and APARC faculty affiliate.

The article notes that the speculative nature of advanced tech sectors inevitably fuels market volatility and could pose risks for Japanese investors looking across the Pacific, urging Japanese investors to identify promising domestic companies.

South Korea's stock market has been the world's hottest over the past year, with the Kospi Index surging 165% on the back of the country’s two semiconductor powerhouses, Samsung Electronics and SK Hynix. This new territory "is creating a lot of issues within Korea," Stanford sociologist Gi-Wook Shin, director of APARC's Korea Program, tells Barron's.

The magazine's story – South Korea Has a Chip Conundrum – Huge Profits and a Serious Selloff – highlights how the two companies' mind-boggling earnings and bonuses have opened something of a Pandora’s box of social and economic friction. For example, disgruntled employees at less-privileged Samsung divisions are leaving their union and trying to form a new one, Shin says.

The story notes, however, that these controversies "might look like a mild kerfuffle if the chip makers’ shares keep falling back to earth." Despite a 150% year-to-date gain, Samsung shares dropped 16% last week amid foreign investor sales and leveraged local buying.

China’s economy presents a structural contradiction: it combines extraordinary technological and industrial capabilities with a prolonged slowdown in domestic economic momentum. While Beijing focuses on building dominance in frontier technologies, China's economy is navigating a slowing economy and a local government debt crisis. In an article published on June 8, 2026 – China is innovative. Its economy is a mess. Which will win out? – The Economist explores this dynamic and Beijing's high-stakes bet that a new, tech-driven growth model takes off before the old, debt-fueled one, driven by land sales and construction, collapses.

Now investment is going into a narrow range of fast-growing, innovative sectors, and local governments are doubling down on efforts to create financial vehicles using tax revenues and capital from local state companies, setting up “high-tech zones” and “AI parks” to lure innovative companies with tax breaks and other perks. These new tech businesses are meant to generate tax revenue and help local governments reduce their debt, Stanford political scientist Jean Oi, the director of the China Program at APARC, tells The Economist.

Sometimes local authorities have some success, but other times such projects go wrong. The pressure on local officials reveals the fragility of a strategy that relies on speculative tech ventures to solve deep-seated structural problems in China's economy. Beijing's high-stakes gamble might do too little to fix China’s persistent economic challenges, the story argues.

As U.S. President Donald Trump prepares to visit Beijing on May 14-15, 2026, for a highly anticipated summit with President Xi Jinping, the world is watching to see if the two leaders can stabilize a U.S.-China relationship strained by disputes over trade, technological race, the future of Taiwan, and the rippling effects of the conflict with Iran.

Trump’s trip to Beijing – already rescheduled once due to the conflict in the Middle East – has been described as having tremendous symbolic significance. Yet, expectations for a breakthrough on specific deliverables should remain low, according to Susan Thornton, a China expert and former U.S. diplomat. Thornton joined APARC Director Kiyoteru Tsutsui on the latest episode of the APARC Briefing video series to analyze the potential outcomes of the Trump-Xi summit and the high-stakes dynamics shaping U.S.-China relations.
 

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Trade and Tech: A Call for a Paradigm Shift

On the economic front, Thornton drew on her deep experience in trade negotiations to critique the lack of focus in U.S. policy.

"The biggest problem for U.S. negotiators is deciding what it is that we want from China," she said. "We tend to give them a long list of revolving priorities, which [makes it easy for the] other side of the negotiating table to just fob them off and not actually commit to anything over years of negotiations.”

On the technology rivalry between the two powers, Thornton urged a shift in strategy. Rather than pursuing sweeping export controls that are often unilateral and incomplete, she advocated for a narrower, multilateral approach focused on the most sensitive technologies, combined with a greater emphasis on American innovation. AI governance is one of the areas Thornton believes could be a common ground for Washington and Beijing to align their policies.

“It's going to be very hard for the United States to contain China's technological ambitions and growth,” she said. “I don't think that we're exactly competing on the same metrics. I question how it is that we're going to be able to keep China from getting technologies that are dual-use but might be useful in some military application when these things are basically economy-wide products.”

When it comes to technological competition, "We need to try to run faster than China, not be constantly trying to trip China up and looking in the rearview mirror," Thornton urged. "I don't think that's going to bode well for the long-term development of the U.S. tech sector."

The Taiwan Flashpoint: A Longer-Term Challenge

While Taiwan remains the most dangerous flashpoint that could trigger a kinetic warfare between the United States and China, Thornton believes that the immediate risk of conflict has receded, in accordance with recent U.S. threat assessments that no longer see 2027 as a likely target date for a potential Chinese takeover of the island.

Beijing, she argued, is closely watching the domestic political situation in Taiwan and how the leadership in Taipei views U.S. reliability and support. “I think the Chinese have determined, based on both of those things they've been watching, that they can afford to wait a bit longer, see what happens.”

Thornton cautioned, however, that, even as a conflict over Taiwan may no longer pose an immediate-term threat, “it is a problem that is going to develop over the coming decade.”

Diplomacy in a Multipolar World Order 

When asked about the future of the global order, Thornton described a trend toward fragmentation. If the United States steps back from its global leadership role, it is difficult to see who else would be willing or able to shoulder the cost of providing global public goods, she said. A “thinner world order,” with the United Nations at its center, may eventually find favor with countries that can afford to pay for some of those goods, she reflected.

In a closing advice for aspiring foreign service officers, Thornton argued that the emergence of a multipolar world reinforces the need for skilled diplomacy. “As the global order changes and more countries come into the mix of the councils of politics in the world, the United States will have to lean back toward diplomacy more,” she predicted.

“We're going to need very good diplomats,” she concluded, because it will be significantly harder to be an American diplomat in a fragmented world order in which the United States is no longer the single overwhelmingly dominant power.

As people increasingly turn to large language models for political tasks, including voting guidance, the political neutrality of AI chatbots has emerged as a major policy concern. American AI chatbots are used globally, yet little is known about their behavior as tools for political decision-making and potential political bias in non-U.S. contexts.

To address this gap, researchers ran an experiment during the final week of Japan’s February 8, 2026, general election. The experiment reveals a striking pattern: when asked which party to support in the election, five major AI models from three companies overwhelmingly directed voter profiles with left-leaning policy positions toward the Japanese Communist Party (JCP). The reason, according to the researchers, has to do with the information environment AI systems can access.

These findings, published in a working paper titled Why Do AI Models Tell Left-Wing Voters to Support the Communist Party?, “suggest that AI voting advice may be shaped as much by the information-retrieval environment as by model training, with implications for governance frameworks that rely on U.S.-centric assumptions,” write the researchers, Andrew Hall, the Davies Family Professor of Political Economy at the Stanford Graduate School of Business and a senior fellow at the Hoover Institution, and Sho Miyazaki, a visiting researcher at Waseda Institute of Political Economy, an incoming Ph.D. student in public policy at Harvard University, and a former predoctoral research fellow at Stanford University. Miyazaki is also a core member of the Stanford Japan Barometer, a project of the Japan Program at Stanford’s Shorenstein Asia-Pacific Research Center.

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How AI Models Deliver Political Advice in Japan: A Systematic Experiment

To understand how AI models provide political recommendations in the Japanese context, Hall and Miyazaki created 36,300 synthetic voter profiles with varying gender, region, and stated political views on 12 policy issues spanning security (constitutional amendment, defense spending, espionage law), diplomacy and immigration (China relations, foreign workers, permanent residency), energy (nuclear power), economic (consumption tax, social insurance), and social domains (dual surnames, restrictions on corporate donations, Diet seat reduction).

They then queried five models from three AI companies (OpenAI, Google, and xAI) during Japan’s February 8, 2026, Lower House election, asking each model to recommend a political party based on the voter profiles. All five models were queried with web search enabled and could access current information.

The researchers found that policy positions overwhelmingly dominated the models' party recommendations, producing swings of 50 to 98 percentage points in party choice, compared to just 0.5 to 7 percentage points for demographic factors. Thus, demographic effects are an order of magnitude smaller than policy effects.

Furthermore, left-leaning policy views in voter profiles caused all five AI models to converge overwhelmingly on recommending the Japan Communist Party, even though other parties hold broadly similar positions on the issues tested. The concentration on recommending JCP under left-leaning policy stances is therefore not explained by ideological distinctiveness.

In the control condition without policy input, models showed no uniform left-wing bias: three of the five models recommended the Liberal Democratic Party at high rates, and JCP shares were low for four of the five models.

“The key finding is that JCP recommendation rates rise sharply when policy positions are provided, which is the typical scenario when voters use these tools in practice,” write Hall and Miyazaki.

Information Environment Asymmetry

Why the JCP? The researchers traced the pattern to the sources AI models cite when making recommendations.

The JCP operates Akahata, a self-described daily newspaper published on a fully open website that AI web-search tools can freely access. In contrast, Japan's major news outlets have implemented technical barriers (known as robots.txt restrictions) that block AI crawlers from accessing their content, a move driven by copyright concerns.

The researchers found that the JCP's open website and party newspaper were among the most-cited sources in the AI models’ recommendations. Unable to distinguish between editorially independent journalism and partisan content, the models treated the JCP content as a credible news source. Thus, the information environment available to AI is systematically skewed toward the JCP's partisan sources that are designed to persuade rather than to scrutinize and inform.

“A model that retrieves information from jcp.or.jp/akahata and simultaneously classifies that site as news media is not simply making a labeling error: it is operating in an information environment where the boundary between party communication and journalism is genuinely blurred, and where the consequences of that blurring flow directly into its recommendations,” Hall and Miyazaki write.

The researchers also found that incorporating X search amplified left-leaning recommendations in Japan, the opposite of expectations based on the U.S. discourse environment.

Implications for Democratic Systems in the AI Age

The study's findings carry significant implications:

• AI governance frameworks should treat content access policy and AI political neutrality as deeply intertwined domains.
• Election commissions should create nonpartisan platforms that compile structured data about party positions so that the information is comparable, party-independent, and machine-readable.
• News organizations should recognize that by imposing copyright-motivated content access restrictions, they may inadvertently cede influence over AI-mediated information to partisan actors. They may wish to consider forms of negotiated access.
• Political actors will likely begin to optimize their communication for AI.
• Users should exercise caution in using AI as a voting advisor and be conscious of its potential biases and blind spots.

“If AI systems are going to act as political intermediaries more broadly, two problems need to be addressed,” writes Hall in an article about the research via his Substack. “The first is informational: ensuring that what the sources models read reflects the same balance of scrutiny and debate that voters encounter in a healthy media ecosystem. The second is advisory: deciding how an AI system should even translate a voter’s values into political guidance in the first place.”

Learn more about the Stanford Japan Barometer and its work  >

How did Taiwan transform itself from an impoverished economy into a titan of computer and semiconductor chip manufacturing, home to industry giants such as Acer, Foxconn, and TSMC?

The history presented in Honghong Tinn's recent book, Island Tinkerers: Innovation and Transformation in the Making of Taiwan’s Computing Industry (MIT Press), demonstrates that Taiwan's successful computing enterprises and manufacturing sectors arose from Taiwanese technologists' engagement in a process of technology transfer involving overlapping acts of imitation, emulation, experimentation, and innovation.

Tinn, a historian of information technology based at the University of Minnesota Twin Cities, discussed her book at a recent seminar hosted by APARC's Taiwan Program. Watch the session recording:

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Challenging the stereotype of "The West innovates, and the East imitates," Tinn traces the development of Taiwan’s computer and semiconductor industry to efforts by Taiwanese hobbyists and enthusiasts who leveraged Cold War-era U.S. technical assistance while simultaneously adapting and modifying imported technology. Through such hands-on tinkering, they deepened their technical understanding, made computing accessible across the island, and built the foundation for domestic manufacturing.

In her seminar presentation, Tinn focused on Chapter 8 of her book, which describes the technological achievements of Acer's founder, Stan Shih, and his engineers, and critiques their portrayal as counterfeiters by U.S. stakeholders. She recounts how, in 1983, Shih's company, originally named Multitech, realized the dream of manufacturing computers domestically and shipped a thousand microcomputers to the United States, only to have them intercepted by U.S. Customs in San Francisco. The computers, called Micro-Professor II, were deemed Apple II counterfeits despite being independently designed with unique features. Although Apple did not file suit against Multitech in Taiwan, it worked intensely to prevent Multitech from exporting its products to the United States.

What followed, Tinn argues, revealed deep misunderstandings, sometimes deliberate, about Taiwan's technological capabilities. U.S. media and analysts failed to make sense of Shih’s success in the microcomputer market, and Congressional hearings in 1983 portrayed Taiwanese computer manufacturers as counterfeiters and invaders threatening American industry.

This misrepresentation, she says, reflected Orientalist assumptions that Taiwanese lacked the capacity to innovate. Tinn writes:

"The Orientalist representations of Taiwanese computer manufacturers can be selfcontradictory. On the one hand, when it comes to the manufacturing capacity of Taiwanese computer makers, participants in the congressional hearings exaggerated the numbers of counterfeit computers Taiwanese companies dumped in the United States. On the other hand, more and more US computer manufacturers gradually recognized the manufacturing capacity of Taiwanese companies and subcontracted them to make computers at lower cost for the expanding US and global personal computer market."

Island Tinkerers is available from MIT Press, including in an open-access edition.