Taiwan's AI Development and Future Strategy: Hardware Entry Ticket Secured, Where's the Next Battle?

30-second overview: On October 8, 2024, the Nobel Prize in Physics was awarded to the physicist who wrote the Hopfield Network and the cognitive scientist who wrote backpropagation¹. The next day, October 9, the Nobel Prize in Chemistry was awarded to three researchers who used AI to solve the 50-year protein folding problem². On May 29 of the same year, NVIDIA CEO Jensen Huang appeared at Taipei's Ningxia Night Market with Morris Chang, Lin Pai-li, and Tsai Li-hsiang, eating oyster omelets. TSMC captured 72% of global wafer foundry revenue; Foxconn, Quanta, and Wistron together produce 90% of the world's AI servers. But in this two-day scientific ceremony—forty-two years of neural network history finally legitimized—no name came from Taiwan. From the Taiwan AI Labs founded by PTT founder Du Yujin, to the government-backed traditional Chinese large language model TAIDE, a bet is being placed from "manufacturing AI" to "becoming AI."

42 Years in the Making: The 2024 Nobel Prizes Awarded in Consecutive Days

On the morning of October 8, 2024, in Stockholm. The Royal Swedish Academy of Sciences announced that the Nobel Prize in Physics for that year was awarded to two AI scientists: John J. Hopfield, 91, a professor emeritus at Princeton University, and Geoffrey Hinton, 76, who had resigned from Google just five months earlier. The prize money was 11 million Swedish kronor, split equally between the two¹.

The selection committee cited the award for "foundational discoveries and inventions that enable machine learning with artificial neural networks"¹. This marked the first time in the history of the Nobel Prize in Physics that the honor was directly bestowed upon the field of neural networks.

The next day, October 9, came the Nobel Prize in Chemistry. Three recipients: David Baker from the University of Washington, along with two individuals from DeepMind, Demis Hassabis and John Jumper. Baker received half of the prize money, while Hassabis and Jumper shared the other half². The citation was divided into two parts, the first recognizing Baker's "computational protein design," and the second honoring Hassabis and Jumper's "protein structure prediction."

Two days, two Nobels, both related to AI. This was unprecedented in the history of the Nobel Prizes.

Looking at the timeline: When Hopfield published his paper "Neural networks and physical systems with emergent collective computational abilities" in the Proceedings of the National Academy of Sciences (PNAS) in 1982, he had just transitioned from condensed matter physics to neuroscience³. From 1982 to 2024, a span of 42 years. The 1986 paper by Hinton and Rumelhart that formalized the backpropagation algorithm as a usable tool⁴ took 38 years from publication to winning the prize. AlphaFold went from its debut at CASP13 in 2018 to winning the Nobel in 2024, a mere six years.

At the end of the day, the recipients honored over these two days were not ChatGPT, but rather a handful of papers from three or four decades ago that few could understand at the time. The gap between fundamental research and industrial application has always been this way.

Geoffrey Hinton, Nobel laureate in Physics 2024, during Nobel Week in Stockholm. Photo: Arthur Petron, 2024-12-08. CC BY-SA 4.0 via Wikimedia Commons.

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The Trillion-Dollar Meal at Ningxia Night Market

On the evening of May 29, 2024, just before the opening of Computex, an unusual group of diners appeared at Taipei's Ningxia Night Market. NVIDIA CEO Jensen Huang was squeezed among the stalls eating oyster omelette with TSMC founder Morris Chang, Quanta Computer Chairman Barry Lam, and MediaTek CEO Allen Tsai⁵. Passersby recognized Huang, and he was instantly surrounded by fans and reporters, creating a scene worthy of star-chasing.

The combined market value of those dining together exceeded several trillion U.S. dollars. But the real story wasn't at the dinner table—it was behind the scenes in the industrial supply chain: the companies these people represent form the physical foundation of global AI computing. During that trip to Taiwan, Huang publicly stated: "Taiwan is one of the most important countries in the world."⁶ This was not mere courtesy. Without Taiwan, the hardware foundation of the AI revolution would not exist.

Jensen Huang was born in Taipei in 1963, spent his childhood in Tainan, and immigrated to the United States at the age of nine⁷. NVIDIA, which he co-founded in 1993, has become synonymous with AI chips. Every advanced GPU designed by NVIDIA—from the A100 and H100 used to train ChatGPT, to the latest Blackwell series—is manufactured by TSMC⁸.

Four months later, when the Nobel Prize lists were announced in Stockholm, not a single name had any connection to that meal. This gap is not a coincidence—it is a structural reality.

Hardware: One Island Powers the AI Revolution

Taiwan's role in the AI hardware supply chain is so pivotal that calling it merely "critical" is an understatement.

In chip manufacturing, TSMC commanded a 72% share of global wafer foundry revenue in 2025⁹. In the most advanced 7-nanometer and below processes, TSMC's market share exceeds 90%. NVIDIA holds roughly 86% of the AI GPU market, and nearly all of these GPUs are manufactured by TSMC¹⁰. The vast majority of the world's computing power used to train and run AI models is born in Taiwan's clean rooms.

Once chips are fabricated, they must be assembled into servers for data centers. This segment is also dominated by Taiwan. The three major ODMs—Foxconn, Quanta, and Compal—collectively produce approximately 90% of global AI servers¹¹. In 2025, each of these three companies surpassed NT$1 trillion in annual revenue (approximately $32 billion USD), with AI server revenue first overtaking consumer electronics in the second quarter¹².

The performance of AI chips depends not only on process miniaturization but also on packaging technology. TSMC's CoWoS (Chip on Wafer on Substrate) advanced packaging technology is key to NVIDIA GPUs achieving their performance targets. In 2026, just NVIDIA alone is projected to demand 595,000 CoWoS wafers, accounting for 60% of global total demand¹³.

Foxconn has also partnered with NVIDIA and the Taiwan government to build a 100-megawatt (MW) AI factory supercomputer in Kaohsiung, utilizing the latest NVIDIA Blackwell architecture¹⁴. Taiwan is upgrading from being "the place that manufactures AI chips" to "the place that runs AI."

Fab 5 at TSMC's Hsinchu campus, the physical site of AI chip foundry manufacturing. Photo: Wikimedia Commons via TSMC Fab 5 file.

The question is: once hardware secures its place at the table, where will the next battle be fought?

📝 Curator's Note

The prevailing narrative is "Taiwan's semiconductor mountain holds up the AI revolution." This framing is convenient but reverses causality. It's the AI revolution that needed GPUs and thus chose TSMC, not that TSMC grew because of the AI revolution. The real tension lies in: when GPUs become commodities, where will the next layer of value shift? The 2024 Nobel Prizes offer one answer—toward the models themselves: the 12 pages written by Hopfield, the night Hinton and his student Krizhevsky used AlexNet in 2012 to slash ImageNet image recognition error rates from 26.2% to 15.3%¹⁵, and the afternoon Hassabis's team got AlphaFold to achieve a median GDT of 92.4 at CASP14.

Hopfield 1982: A Memory Model Written by a Physicist

In 1982, John Hopfield, a condensed matter physicist at Princeton, wrote a 12-page paper with a long title: Neural networks and physical systems with emergent collective computational abilities, published in the Proceedings of the National Academy of Sciences³.

What he did was essentially translate "memory" into physics.

There's a concept in physics called spin glass: a collection of magnetic atoms, each with its own spin direction, interacting with one another, and the entire system spontaneously finding a point of lowest energy. Hopfield took this idea and applied it to neurons: imagining neurons as spins, and connection strengths as interactions, the entire network would spontaneously converge to a stable state — an "energy minimum"³. Each energy minimum represents a stored memory.

The elegance of this model lies in making memory something describable in physical language. Given incomplete cues, the network would automatically find the closest energy minimum and complete the entire memory. This is the mathematical ancestor of what generative AI does today.

In 1982, Taiwan's electronics industry was just getting started, and TSMC had not yet been founded. Morris Chang wouldn't establish the company that would become the "guardian deity of the nation" until 1987. By 2026, Hopfield's paper had accumulated over 27,000 citations on Google Scholar¹⁶.

Even more interesting is something Hopfield said later. He spent his entire career in condensed matter physics at Princeton, and crossing into neuroscience was seen as a "hobby" by his contemporaries at the time. When the 2024 Nobel Prize list was announced, at age 91, the Royal Swedish Academy of Sciences asked him about his feelings during a phone interview, and he expressed unease about "no one understanding or controlling the direction of AI"¹⁷.

The person who laid the mathematical foundation of modern AI reminded everyone to be cautious on the very day he received his award.

_John J. Hopfield, 2024 Nobel Prize in Physics winner, Nobel Week in Stockholm. Photo: Arthur Petron, 2024-12-08. CC BY-SA 4.0 via Wikimedia Commons._

Hinton: The 1986 Paper and the 2023 Warning Upon Leaving Google

Geoffrey Hinton, born in 1947 in London, Ontario, is another person who was belatedly recognized by history 38 years later¹⁸.

In 1986, Hinton, along with David Rumelhart and Ronald Williams, published a paper on backpropagation in《Nature》⁴. The algorithm works by, when a neural network makes an error, sending the error signal backward through each layer, adjusting the connection weights layer by layer. This is how all modern deep learning models train themselves.

The algorithm was written in 1986, but it required three conditions to explode: sufficiently cheap computing power, enough data, and people willing to believe in this path. The first two were ready in the early 2010s, and the representative figures for the third were Hinton and his two students, Alex Krizhevsky and Ilya Sutskever. In 2012, their GPU-trained convolutional neural network, AlexNet, won the ImageNet image recognition competition with a top-5 error rate of 15.3%, far surpassing the second place's 26.2%¹⁵. That moment, the entire industry finally believed backpropagation could actually work.

In March 2013, Google acquired Hinton's small company DNNresearch for $44 million, bringing the 65-year-old into its fold¹⁸. For the next decade, he was the most respected AI scholar in Silicon Valley.

Then, on May 1, 2023, The New York Times published an interview: Hinton had left Google.

His reason for leaving was not retirement. In the interview, he said he wanted to "be able to speak freely about AI risks without considering the impact on Google"¹⁹. The things he warned about included: AI systems might soon become smarter than humans, could be used by bad actors to do harm, and that "it's hard to see what can be done to stop this"¹⁹. He even said he felt "a part of regret" about his life's work¹⁹.

When he was awarded the 2024 Nobel Prize in Physics, he reiterated the warning during a phone interview: to be cautious of the possibility of AI going out of control²⁰.

The person who wrote the deep learning training algorithm and the person who wrote the memory model stood on the stage of the Royal Swedish Academy of Sciences on the same day in October 2024, both warning everyone to be cautious of this thing that might be more dangerous than imagined. This scene has a sense of parity with Oppenheimer's expression when he watched the mushroom cloud rise in the New Mexico desert in 1945.

Two months later, on December 8, 2024, Hinton delivered his Nobel Prize lecture at the Aula Magna of Stockholm University. The title was "Boltzmann Machines" — an early work he and Hopfield, in a related line of thought, that incorporated thermodynamic probability distributions into neural networks. After listening, it became clear that the 1986 backpropagation paper was not an isolated insight but part of a whole set of ideas that emerged from the intersection of physics and cognitive science in the 1980s:

Royal Swedish Academy of Sciences official channel: Geoffrey Hinton's 2024 Nobel Prize in Physics lecture on December 8, "Boltzmann Machines". From the 1980s when he and Sejnowski wrote Boltzmann machines, to backpropagation, to today's LLMs — a span of forty years. In the last five minutes, he reiterated his concerns about AI risks, this time standing on the Nobel stage.

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From PTT to AI Labs: Du Yujin's Two Ventures

Back to this island called Taiwan. At the same time Hopfield was writing his memory model, Taiwan had only just begun to have computer science departments.

In 1995, second-year computer science student at National Taiwan University Du Yujin set up PTT on a 486 computer using open-source software in his dormitory. It later became Taiwan's largest electronic bulletin board. Thirty years later, PTT still sees hundreds of thousands of users online daily, a living fossil of Taiwan's internet culture.

Du later joined Microsoft and participated in the development of the Cortana voice assistant. In April 2017, he gave up a high salary in Silicon Valley and returned to Taiwan to found "Taiwan AI Labs" (Taiwan AI Labs), Asia's first non-profit, open AI research institution²¹.

His motivation was straightforward: Taiwan has world-class software talent, but all this talent runs off to Silicon Valley. He wanted to build a platform so that those who want to come back or stay have a place to do AI research.

Taiwan AI Labs' most famous product is "Yating Transcript," a speech recognition system optimized for Traditional Chinese and Taiwanese accents. During the COVID-19 pandemic, the lab also developed fake news detection tools and federated learning medical AI²². The common thread of these projects is that they solve local Taiwanese problems using local Taiwanese data, rather than simply translating American models for use.

Du Yujin's story—from PTT to AI Labs—is in some sense a microcosm of Taiwan's software development: not lacking in technical ability, but lacking an ecosystem that retains talent.

💡 Did you know?

In 1986, the year Hinton published backpropagation, Taiwan's GDP was approximately US$7.79 billion, per capita GDP about US$4,007, and the Hsinchu Science Park had just been operating for six years²³. These three events occurred simultaneously on the same Earth, but it would take another 26 years before this historical timeline converged on the ImageNet dataset. The timescale of fundamental research is always longer than the industrial narrative feels.

AlphaFold: Half a Nobel for a 50-Year Protein Folding Puzzle

The story of the 2024 Nobel Prize in Chemistry begins with a question posed in 1972.

During his Nobel Prize acceptance speech that year, American biochemist Christian Anfinsen put forward a hypothesis: the three-dimensional folded structure of a protein is entirely determined by its amino acid sequence²⁴. If this hypothesis holds, then theoretically, once we see an amino acid sequence, we should be able to calculate the corresponding 3D structure. But that "should" remained unfulfilled for half a century. Protein folding was dubbed a "grand challenge." The academic community held the CASP competition every two years, comparing everyone's predictions against experimental structures. Since its inception in 1994, it had been held 13 times, and no one managed to break through²⁵.

Until CASP13 in 2018, when DeepMind sent its first-generation AlphaFold to compete and win, though the accuracy was still not practically useful. The real turning point came on November 30, 2020, at CASP14: AlphaFold 2 achieved a median GDT score of 92.4²⁵. A GDT of 92.4 means that for more than half of the predictions, the deviation of atomic positions from experimental values was less than one ångström, reaching the level of experimental resolution. John Moult, the organizer of CASP, said on that day, "To a large extent, this problem has been solved"²⁵.

A problem that remained unsolved for 50 years was cracked in six years by a research team in London.

Things moved even faster after that. In July 2021, AlphaFold 2's source code was open-sourced; the same year, DeepMind partnered with the European Molecular Biology Laboratory (EMBL-EBI) to create a public database of protein structures predicted by AlphaFold. By July 2022, this database covered 100 million species, containing approximately 200 million protein structures, effectively releasing free 3D models of almost all known proteins on Earth²⁶.

On May 8, 2024, DeepMind published AlphaFold 3 in Nature, expanding its predictive capabilities from single proteins to the interactions between proteins and DNA, RNA, ligands, and ions²⁷. From new drug development and vaccine design to enzyme engineering, every field that needs to know how molecules fit together has been fundamentally rewritten by this tool.

Demis Hassabis, the mind behind AlphaFold, is not a traditional biochemist. He started playing chess at age four and earned the title of master at 13; at 17, he co-developed the simulation game Theme Park with Peter Molyneux, selling millions of copies²⁸. In 2010, he co-founded DeepMind in London with Shane Legg and Mustafa Suleyman; in 2014, Google acquired it for £400 million²⁸. In 2016, DeepMind's AlphaGo defeated Lee Sedol; in 2020, AlphaFold 2; in 2024, the Nobel Prize—three events spaced less than a decade apart.

The common thread running through these achievements is the same bet: using neural networks to solve problems that human brains could not crack. Go is a closed-rule game, while protein folding is an open-rule but physically constrained challenge. Hassabis chose the right battlefield for both.

On Taiwan's side, the glycan-protein research established during the tenure of Academia Sinica President Weng Chi-hui (2006–2016) represents the closest academic investment to this frontier²⁹. Teams at the Institute of Biological and Medical Sciences and the Institute of Biochemistry are also conducting downstream research using AlphaFold's open-source weights. However, Taiwan currently lacks the corresponding institutional framework for developing core models at the AlphaFold level.

⚠️ Controversial Viewpoint

AlphaFold's Nobel Prize in Chemistry has sparked debate in academia: some structural biologists believe the award should have gone to those who conducted the earliest critical experiments in X-ray crystallography or nuclear magnetic resonance, rather than elevating a computational tool to the realm of chemistry³⁰. Others consider this debate itself outdated—when an algorithm can complete the 3D structures of nearly all proteins on Earth within five years, that is chemistry. After October 2024, the debate gradually shifted toward the latter view, but the tension it represents has not disappeared: as AI's capabilities expand, should the boundaries of traditional disciplines be redrawn?

TAIDE: Why Taiwan Needs Its Own Language Model

Six months after ChatGPT swept the globe in April 2023, Taiwan's National Science and Technology Council (NSTC) launched the "TAIDE" initiative, officially standing for Trustworthy AI Dialogue Engine³¹.

Why does an island nation of 23 million people need to build its own large language model?

The reasons go beyond technological autonomy. Traditional Chinese makes up a tiny fraction of global AI training data, with most Chinese-language content coming from Simplified Chinese websites. When Taiwanese users interact with ChatGPT or other models, the responses often reflect mainland China's terminology habits and viewpoint assumptions. "Video" instead of "film," "quality" instead of "quality"—these seemingly minor differences point to deeper questions of cultural subjectivity. Common Wealth Magazine directly headlined its coverage as "Preventing Chinese AI Cultural Invasion" in reporting on TAIDE³².

In April 2024, the TAIDE team released the commercial version TAIDE-LX-7B and the academic version TAIDE-LX-13B models, performing well on tasks such as writing, translation, and summarization³³. By 2026, TAIDE 2.0 was launched alongside the MediaTek-supported Breeze-8B model, moving Taiwan's LLM ecosystem from the "catching up" stage to the "usable" stage³⁴.

Even more interesting is the flourishing of applications. National Chung Hsing University used TAIDE to create an agricultural knowledge retrieval system called "Shennong TAIDE"; National University of Tainan developed a Mandarin-English dialogue robot for Taiwanese Hokkien language instruction; National Yang Ming Chiao Tung University trained Hokkien and Hakka versions of the TAIDE model³⁵. These applications confirm one thing: language models are simultaneously technical products and cultural carriers. An AI that doesn't understand "Tianchuan Festival" and "Mazu pilgrimage" cannot truly serve the people of Taiwan.

However, TAIDE's scale remains small: the commercial 8B and academic 13B parameter counts are two orders of magnitude smaller than OpenAI's GPT-4 level (estimated at over 1 trillion parameters). This gap stems from GPU budget constraints rather than capability limitations. Training a cutting-edge LLM requires computing power costing hundreds of millions of dollars, on par with a national-level scientific research institution's annual budget.

Hacked Into AI Security

Taiwan is one of the countries in the world most frequently targeted by cyberattacks. This unfortunate reality has unexpectedly given birth to a formidable AI cybersecurity industry.

Founded at the end of 2017, CyCraft, Taiwan's first cybersecurity company combining AI with endpoint monitoring, has had its technology included in reports by global research and advisory firm Gartner seven times, making it the only Taiwanese company to have passed the US MITRE ATT&CK authoritative evaluation three times³⁶. In February 2026, CyCraft was listed on the Taipei Exchange's Innovation Board, becoming the first Taiwan-based AI cybersecurity software company with international-grade autonomous R&D capabilities³⁷.

CyCraft's clients include Taiwan government agencies, defense units, banks, and semiconductor companies — precisely the targets most frequently locked onto by state-sponsored hackers. The company has subsidiaries in Japan and Singapore and is exporting its "real combat experience from being hacked" across the entire Asia-Pacific region.

This case illustrates one point: Taiwan's AI advantage does not come only from semiconductors, but also from the practical combat capabilities honed by its special geopolitical situation.

Policy: From the "AI-First Year" to the Ministry of Digital Affairs

Taiwan's AI policy development can be understood through three key milestones.

2017 to 2018 marked the foundational phase. Ministry of Science and Technology (MOST) Minister Chen Liang-chi declared 2017 the "AI-First Year" and proposed the "Small Country, Big Strategy for AI"³⁸, acknowledging Taiwan's small market size but emphasizing three key strengths: semiconductor manufacturing, the ICT supply chain, and a talent pool of science and engineering professionals. In 2018, the first phase of the "Taiwan AI Action Plan" was launched, with over NT$40 billion invested over four years, focusing on building AI computing infrastructure known as the "Taiwan AI Cloud" (TWCC)³⁹.

In 2022, AI policy became institutionalized. The Ministry of Digital Affairs (MODA) was established, consolidating digital affairs previously scattered across the Ministry of Science and Technology, the Ministry of Economic Affairs, and the Ministry of Transportation. This step elevated AI policy from a "project under the science ministry" to a "cross-ministerial national strategy." That same year, the government released the "Artificial Intelligence Development Guidelines," emphasizing human-centered principles, transparency and explainability, and fairness without discrimination.

From 2023 onward, the focus shifted to generative AI. The impact of ChatGPT forced a rapid policy pivot. The TAIDE project was launched, the draft AI Basic Law was promoted, and public sector AI adoption accelerated. Taiwan's strategy has been pragmatic: rather than competing with the US and China in the quantity of foundational research papers, it has focused on integrating AI with existing manufacturing strengths. Smart manufacturing, medical imaging, and semiconductor yield prediction are all areas where Taiwan has data, real-world use cases, and competitive advantages.

The problem is that when the Nobel Prize winners were announced in October 2024, none came from the "smart manufacturing" pathway.

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Anxiety: The Software Gap in the Hardware Empire

Behind the glittering numbers lies a structural problem in Taiwan's AI development: a severe imbalance between hardware and software.

Taiwan produces 90% of the world's AI servers and most of the AI chips, yet its presence is minimal in the "soft" areas of AI model development, data ecosystems, and platform software. Among the world's top 20 AI models—including GPT, Claude, Gemini, and LLaMA—none come from Taiwan. Comparing the 2024 Nobel Prize-winning work, from Hopfield Networks and backpropagation to AlphaFold, these three lines of research are far removed from Taiwan's industry.

This is an old problem in a new form. When TSMC engineers can earn over NT$2 million annually, software startups struggle to attract top talent. Google, Microsoft, and NVIDIA have established R&D centers in Taiwan, with compensation and benefits creating a powerful pull. A National Taiwan University computer science graduate's first choice is often a foreign company or TSMC IT, rather than joining a local AI startup.

An even more fundamental challenge is data. The value of AI models comes from training data, and the volume of high-quality Traditional Chinese data pales in comparison to English or Simplified Chinese. The text output of Taiwan's 23 million people is naturally no match for the English-speaking world or mainland China. The TAIDE project attempts to address this issue, but the scale disadvantage of data is structural.

Taiwan's real bet lies in vertical applications rather than foundational models: instead of confronting OpenAI or Google head-on on general models, Taiwan chooses to find irreplaceable positions in semiconductor process AI, medical imaging AI, cybersecurity AI, and Traditional Chinese NLP. These fields offer Taiwan unique data and scenario advantages that others find difficult to replicate.

An Island's AI Choice

In 2026, Taiwan stands at a unique position: it has never been more indispensable in the AI hardware supply chain, yet it remains on the periphery of the AI software ecosystem.

This is not entirely a bad thing. Historically, Taiwan's success model has always been "not being the brand, but being the brand behind the brand." The pure OEM model invented by Morris Chang in 1987 made TSMC one of the world's top ten companies by market value. Today, the same logic is being replayed in the AI server industry: Foxconn does not build AI models, but every AI model in the world runs on servers assembled by Foxconn.

However, the rules of the game in the AI era may be different. As value shifts from hardware to software and data, the profit margins for pure OEM manufacturing will be compressed. The two Nobel Prizes awarded in 2024 went entirely to people working on the software layer. Hopfield wrote mathematical models, Hinton wrote training algorithms, and Hassabis wrote biological solutions. These works all run on hardware manufactured in Taiwan, but the awards are not given to hardware.

Taiwan needs to grow software and data capabilities on top of its hardware dominance: hardware remains the foundation, and new value layers are stacked upon it. TAIDE is an attempt, CyCraft is an attempt, and Taiwan AI Labs is an attempt. Their common thread is: not striving to build "the world's largest AI," but rather "the AI that understands Taiwan best."

Forty-two years ago, when Hopfield wrote those 12 pages at Princeton, no one knew it would become the mathematical foundation of today's memory models. Fifty years ago, when Anfinsen proposed the protein folding hypothesis in his Nobel lecture, no one anticipated it would take until that afternoon in 2020 for a group of Londoners to crack it. The timescale of fundamental research is longer than any Computex.

The meal eaten at the Ningxia Night Market represents the position Taiwan has accumulated over these 42 years. The next battle is not fought at the oyster omelet stall, but in whether Taiwan has the courage to let a student writing code in an NTU dormitory win the island's Nobel Prize twenty or thirty years from now.

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Extended Reading:

• The Rise of the AI Island Nation: Taiwan's AI Development and Future Strategy — An early version of the policy framework narrative, covering the AI Action Plan, the five strategic areas, and how the "semiconductor guardian" integrates with the AI revolution.
• Taiwan AI Labs — The complete journey of Du Yujin from PTT to AI Labs, including the TAIDE / TAME / FedGPT open-source language model ecosystem.
• Taiwan AI School — The unfinished phone call and the AI military academy established with NT$18 million in private fundraising by Chen Shengwei: the story of cultivating over ten thousand alumni in eight years.
• Taiwan AI Daily Life — A documentary of generative AI entering everyday life in Taiwan, from convenience store ordering to NHI batch reviews.
• Taiwan Enterprise: TSMC — The global leader in wafer foundry, the core of AI chip manufacturing, from Morris Chang's pure OEM model to advanced packaging.
• Semiconductor Industry — The full picture of Taiwan's semiconductor ecosystem, from IC design to packaging and testing.
• Development of Taiwan's Cybersecurity Industry — How geopolitical pressures have given birth to a Pan-Pacific-level AI cybersecurity industry.

Image Sources

This article uses 4 public domain / CC-licensed images, all cached in public/article-images/technology/ to avoid hotlinking source servers:

• Estructura tridimensional de la proteïna CBLN1 per AlphaFold amb codificació rainbow — hero, CBLN1 protein AlphaFold predicted structure, rainbow color coding N→C terminus. Photo: BQUB25-UPoch (own work, AlphaFold + PyMOL), 2025-11-15, CC BY 4.0.
• Geoffrey E. Hinton, 2024 Nobel Prize Laureate in Physics (3x4 cropped) — inline, 2024 Nobel Week Hinton official portrait. Photo: Arthur Petron, 2024-12-08, CC BY-SA 4.0.
• John J. Hopfield, 2024 Nobel Prize Laureate in Physics 1 (cropped) — inline, 2024 Nobel Week Hopfield official portrait. Photo: Arthur Petron, 2024-12-08, CC BY-SA 4.0.
• TSMC Fab 5 — inline, TSMC Hsinchu Fab 5 plant, AI chip contract manufacturing physical site. Photo: Wikimedia Commons (existing cache).

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References

1. The Nobel Prize in Physics 2024 press release — Officially announced by the Royal Swedish Academy of Sciences on October 8, 2024. Original text: 'The Royal Swedish Academy of Sciences has decided to award the Nobel Prize in Physics 2024 to John J. Hopfield and Geoffrey Hinton for foundational discoveries and inventions that enable machine learning with artificial neural networks.' The prize money is SEK 11 million, shared equally between the two.↩²³
2. The Nobel Prize in Chemistry 2024 press release — Announced on October 9, 2024. The prize money is SEK 11 million; David Baker received half 'for computational protein design', while Demis Hassabis and John Jumper shared the other half 'for protein structure prediction'.↩²
3. PNAS, 79(8), 2554-2558 — Hopfield, J. J. (1982). "Neural networks and physical systems with emergent collective computational abilities."↩²³
4. Nature, 323, 533-536 — Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). "Learning representations by back-propagating errors."↩²
5. Tom's Hardware: Semiconductor legends take a stroll in a Taiwanese night market — May 29, 2024 report on Ningxia Night Market scene, recording the dining scenes of Huang Rongxian, Zhang Zhongmu, Lin Bailing, and Cai Lixing at the same table.↩
6. Taiwan News: Nvidia CEO calls Taiwan 'one of the most important countries in the world' — Public remarks by Huang Rongxian during his visit to Taiwan on May 30, 2024.↩
7. Wikipedia: Jensen Huang — Biographical information about Huang Rongxian, born in Taipei in 1963, raised in Tainan, and immigrated to the United States at the age of nine.↩
8. Klover.ai: TSMC AI Fabricating Dominance — All advanced GPUs from NVIDIA (A100, H100, Blackwell series) are manufactured by TSMC. See↩
9. SQ Magazine: AI Chip Statistics 2025 — Data source for TSMC's 72% share of global wafer foundry revenue in 2025; also see Motley Fool's concurrent report.↩
10. PatentPC: The AI Chip Market Explosion — Source of NVIDIA's 86% market share in AI GPUs.↩
11. Tech-Now: Taiwan Leads Global AI Server Shift, Surpassing iPhones in 2025 — Data on Foxconn, Wistron, and Quanta accounting for 90% of global AI server shipments.↩
12. DigiTimes: Foxconn, Wistron, Quanta to sustain trillion-dollar revenue on AI server in 2026 — Report on the three ODMs achieving over a trillion in annual revenue and AI servers surpassing consumer electronics.↩
13. 36Kr: Who Will Divide Up the CoWoS Production Capacity in 2026? — NVIDIA's demand for 595,000 CoWoS wafers, accounting for 60% globally.↩
14. NVIDIA Newsroom: Foxconn Builds AI Factory in Partnership With Taiwan and NVIDIA — Kaohsiung 100MW AI factory cooperation project; also see CNBC report on 100MW power capacity.↩
15. NeurIPS 2012 / NIPS Proceedings — Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). "ImageNet Classification with Deep Convolutional Neural Networks."↩²
16. PanSci: 2024 Nobel Prize in Physics — Hopfield and Hinton Open the Era of Machine Learning with Artificial Neural Networks — Content Curation Partner per MOU 2026-05-05. Covers the background of the Hopfield Network proposal, the spin glass analogy, cumulative citation counts of the paper, and mathematical connections to contemporary deep learning.↩
17. The Guardian: Nobel physics prize 2024 winner John Hopfield warns of AI dangers — Telephone interview coverage of the 2024 Nobel Prize in Physics on October 8, where Hopfield and Hinton both issued warnings about AI risks on the same day.↩
18. Wikipedia: Geoffrey Hinton — Hinton was born in London, Windsor, on December 6, 1947, and joined Google in March 2013 after Google acquired DNNresearch for $44 million.↩²
19. BBC News: AI 'godfather' Geoffrey Hinton warns of dangers as he quits Google — On May 1, 2023, after leaving Google, Hinton expressed concerns about AI risks to the BBC. The original quotes include 'I left so that I could talk about the dangers of AI without considering how this impacts Google' and 'a part of me now regrets my life's work.' Related NYT interview details are also referenced in this report.↩²³
20. Nature: AI scientist Geoffrey Hinton wins Nobel prize for physics — Nature provides a detailed account of the 2024 Nobel Prize in Physics ceremony and a phone interview with Hinton.↩
21. Taiwan AI Labs Official Website: About Us — Official introduction of Du Yujin, who founded PTT at National Taiwan University in 1995 and returned to Taiwan to establish Taiwan AI Labs in April 2017.↩
22. TechNews: AI Talent in Taiwan, Should They Stay or Leave? An Interview with Taiwan AI Labs Founder Du Yujin — Complete transcript of an interview with Ya Ting, introducing core projects such as federated learning medical AI.↩
23. Wikipedia: Economic history of Taiwan — 1986 Taiwan GDP data; the Hsinchu Science Park was established in December 1980.↩
24. Science, 181(4096), 223-230 — Anfinsen, C. B. (1973). "Principles that govern the folding of protein chains."↩
25. Nature: 'It will change everything': DeepMind's AI makes gigantic leap in solving protein structures — Reported on November 30, 2020, the results of CASP14 were announced, with AlphaFold 2 achieving a median GDT of 92.4. CASP organizer John Moult commented, 'in some sense the problem is solved.'↩²³
26. DeepMind: AlphaFold reveals the structure of the protein universe — On July 28, 2022, it was announced that the AlphaFold Protein Structure Database covers 100 million species and approximately 200 million protein structures.↩
27. Abramson, J., Adler, J., Dunger, J. et al. (2024). Accurate structure prediction of biomolecular interactions with AlphaFold 3. Nature 630, 493-500 — AlphaFold 3 was released on May 8, 2024, expanding predictions to protein-DNA/RNA/ligand/ion complexes.↩
28. Wikipedia: Demis Hassabis — Hassabis began playing chess at age 4, co-developed 'Theme Park' with Peter Molyneux at age 17 (1994), founded DeepMind in London in 2010, and was acquired by Google for approximately £400 million in 2014.↩²
29. Academia Sinica Genomic Research Center — During Director Weng Qihui's tenure (2006-2016), the Glycobiology and Protein Structure Research Center was established.↩
30. PanSci: 2024 Nobel Prize in Chemistry — David Baker, Demis Hassabis, John Jumper solve the protein folding problem — Content Curation Partner per MOU 2026-05-05. Covers the AlphaFold Nobel Chemistry controversy and discussions on disciplinary boundaries between structural biology and computational chemistry.↩
31. Executive Yuan: Enhancing Taiwan's AI Infrastructure — Building a Trusted AI Dialogue Engine TAIDE — Official explanation of the TAIDE program launched in April 2023.↩
32. Common Wealth Magazine: 'Preventing Chinese AI Cultural Invasion' — What Can Taiwan's First Traditional Chinese Large Language Model TAIDE Do? — TAIDE themed report, source of discourse on cultural subjectivity of Traditional Chinese LLMs.↩
33. National Science and Technology Council Press Release: TAIDE Achieved Results in One Year, Public-Private Partnership to Promote a Large Language Model with Taiwanese Characteristics — April 2024 release of TAIDE-LX-7B commercial version and 13B academic research version.↩
34. CloudInsight: Taiwan LLM Development Status 2026 — A complete survey of Taiwan's LLM ecosystem, including TAIDE 2.0 and Breeze-8B.↩
35. The same CloudInsight report as above. Detailed application cases such as National Chung Hsing University's 'Shennong TAIDE', the English-Taiwanese dialogue robot at Southern Taiwan University of Science and Technology, and the Taiwanese Hokkien TAIDE model at National Yang Ming Chiao Tung University.↩
36. CIO Taiwan: Survey of Taiwan's Cybersecurity Vendors — OYI Intelligence — Details on OYI Intelligence being listed in Gartner seven times and passing the MITRE ATT&CK assessment three times.↩
37. OYI Intelligence Official Website: Listing on the Innovation Board — OYI Cybersecurity Crowned AI Champion! — News release for the February 5, 2026 listing on the Innovation Board.↩
38. Semiconductor Moonshot, Tech Grand Prix — Chen Liang-chi: 1.6 Billion to Compete for Taiwan's AI — Global Views Monthly, 2017 — Minister of Science and Technology Chen Liang-chi declared 2017 the first year of AI and proposed the 'small country, big strategy' for AI in mid-August.↩
39. NSC: AI Science and Technology Strategy — Policy framework including the first phase of the Taiwan AI Action Plan with a budget of NT$40 billion and the construction of TWCC.↩