Trends & Emerging Technology

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Setting aside Taiwan’s AI competitiveness ranking, Robin points to trillion-scale AI infrastructure and space-based computing-center plans that have left Taiwanese suppliers extremely busy, while Taiwan’s stock market keeps reaching new highs. In his view, the United States leads the AI wave, but Taiwanese companies selling the equivalent of shovels, carts, funnels, and jeans on the sidelines are prospering too. The post includes a news link to the full text of Jensen Huang’s GTC speech. Robin’s comment is a personal observation, not a statistical finding.

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Robin recalls that the internet and mobile-internet eras produced many SaaS and sharing-economy applications on infrastructure such as AWS, Azure, and GCP. People expected the AI era to produce a similar abundance of applications on top of large models. His intuition, however, is that model providers are expanding their layer and continually launching applications of their own, seeking almost every application with meaningful scale. The value layer left for small software developers is thin, undifferentiated, and easily replaced. He explicitly says this is a personal impression that may be wrong. If it proves right, software in the AI era may look completely different from the SaaS era, with large players becoming even larger.

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Robin recounts a conversation with a startup CEO. AI may provide a certain level of translation and communication in the future, but people still need very strong command of at least one language to think deeply, analyze, organize, ask AI questions, and express themselves clearly. The CEO believes that after thirty years of science and engineering overshadowing the humanities, AI’s ability to perform basic logical operations will shift human value back toward feeling, understanding, imagination, and empathy. He sees a future for humanities graduates. The Industrial Revolution freed human physical labor; AI will free mental labor, requiring society’s underlying operating logic to be rebuilt. This is a retelling of a conversation, not a statistical or research finding.

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The post reprints a Liu Run article. Chinese companies traditionally refined value for money and supply-chain capabilities at home before taking those advantages abroad. AI is showing a different pattern. Shenzhen-based Plaud AI, for example, entered Europe and the United States first and sold more than one million devices within two years, according to the article; this figure has not been independently verified. AI agents have high computing costs, while Europe and the United States have a mature SaaS subscription culture and customer spending sufficient to cover those costs. Domestic payment habits are less developed. Robin adds that choosing between succeeding locally first and starting in the most promising market tests a founder’s thinking. Either way, build a secure base before expanding rather than spread yourself too thin.

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The excerpt discusses “slow entrepreneurship” in deep tech. Compared with the lightning expansion sought by internet and platform businesses, deep tech emphasizes taking time to build technology and using substantive innovation to empower industry. Real breakthroughs require the patience to endure long, lonely work. It sets out three points: long-term investment needs full-cycle support from incubation through acceleration to commercialization, with room for failure; supply-chain collaboration should connect large enterprises, small and midsize companies, and research institutions; and capital should be patient, with venture funds providing long-term support. Robin closes with one line: the faster the world moves, the calmer the mind. The excerpt’s source remains unconfirmed; it is not Robin’s original writing.

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The post reprints an interview with a venture investor. Judging trends is the most important investment skill. A genuine trend must meet at least three conditions: last more than ten years, change industry structure, and change user behavior. The biggest trap in venture capital is not competition but demand that does not exist, so ask whether a need is real, how long it will last, and how large the market can become. The piece also discusses the explosion of AI-generated content and data. Robin adds that pigs can fly in a strong wind, but when the wind stops, the ground is littered with their bodies. Better to understand how the world is changing and prepare for the future, so that you are in position when the wind rises and the wave arrives. The reprinted material is not Robin’s original writing.

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The post reprints a Dedao Headlines discussion of K-shaped divergence: one line rises and the other falls, moving increasingly far apart. This is not a recurring cycle but a fork in the road, so the instinct to “wait until things improve” can become costly. It offers three reminders: identify which line you are on; do not try to offset a wrong direction with effort—first see where opportunities have moved; and do not hide your situation behind averages. The trends driving divergence include reduced reliance on human labor and the rise of AI and semiconductors. Figures are as stated in the article, as of 2026, and have not been independently verified. Robin adds that each shift—from agriculture to industry, then the knowledge economy, and now AI—has created divergence. Besides working hard, we need to see which line we are on.

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Robin argues that Taiwan does not lack money; it lacks increasingly scarce human resources. A small country cannot pursue everything and must focus, make trade-offs, and define its position clearly. He estimates approximately 90,000 births in Taiwan in 2026; this is his estimate and has not been independently verified. Looking twenty-four years ahead to 2050, if semiconductors and electronics are to keep growing, even the entire 2026 birth cohort might not meet their workforce needs. Taiwan still needs domestic supporting industries, including food service, finance, entertainment, retail, healthcare, education, defense, public administration, police, and fire services.

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Robin cites reported figures: Taiwan would have just over 130,000 births in 2022, compared with nearly 270,000 university entrants in 2015 and roughly 240,000 in 2017. By 2040, the incoming cohort would fall to about 130,000; he argues that university numbers would have to fall by at least half relative to 2015. Universities should move beyond enrollment numbers toward developing each student’s global competitiveness and mobility. Just as investment should be assessed not only for profitability but for impact, greater impact can create greater future value.

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As ChatGPT spreads, Robin argues that even the foundations of education must change. If basic education in Taiwan still force-feeds knowledge through large volumes of text, it cannot outperform ChatGPT. AI education and understanding must begin before university. Government investment in higher education and industry matters, but primary schools need to move beyond one-dimensional text-based rote learning and help children understand the world more fully. He urges the Ministry of Education to consider how children can enjoy learning, learn in diverse ways, explore independently, and build coherent systems of thought and values.

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Robin sees the exhaustion of Taiwan’s demographic dividend as its greatest economic challenge, expressed in super-aging, fewer births, and crises facing labor and health insurance funds. His solutions are greater investment and bold innovation. He recalls two dividends: annual births above 400,000 in the 1960s, together with military personnel and civilians arriving from China; then Taiwanese businesses using China’s rural labor supply. With fewer than 140,000 births in 2022, he argues that Taiwan cannot sustain future growth on births alone. Put the wealth accumulated during those two dividends into new-era ventures and use people, resources, and markets worldwide.

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At the time of the post, Japan had become the fifth country to land on the Moon. Robin recounts a conversation the previous day with two aerospace experts, using Taiwan’s automotive industry as an example. Its domestic market was too small to support a vehicle manufacturer at scale, but the process helped develop many globally competitive component suppliers. He sees a possible parallel in aerospace and satellites. Launching a Taiwan-made rocket into space might not prove cost-effective, yet developing several influential suppliers of critical components along the way would still be a benefit. The difficulty of the task is precisely why it is worth attempting: confronting difficult challenges can help us become better versions of ourselves.

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Watching Starship’s fourth flight, Robin finds the efficiency of Elon Musk and SpaceX astonishing. He writes that operating efficiency and cost problems at Boeing and Lockheed Martin allowed SpaceX to catch up and win NASA contracts, and that the two companies formed ULA in 2006 to pursue SpaceX. He asks whether humanity might return to the Moon in 2025. He also reflects on our good fortune in witnessing an era of remarkable entrepreneurs—Jobs, Gates, Bezos, Musk, Jensen Huang, Zuckerberg, and Page—with the torch of innovation and entrepreneurship continuing to pass from one to the next.

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The post relays reported figures for Transsion Holdings: first-half 2024 revenue of RMB34.558 billion and net profit attributable to the parent of RMB2.852 billion, up 38.07% and 35.70% year over year, respectively. Lei Jun also said Xiaomi would increase its investment in Africa, where its second-quarter smartphone shipments grew 45% year over year and market share reached about 12%. Robin comments that Chinese manufacturers have long been expanding beyond developed markets into Africa and Latin America, where competition is also intensifying. He finds Transsion’s decision to take a different route into Africa while others fought over familiar markets both remarkable and inspiring.

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A conversation with a corporate VC professional in electronics leads Robin to reflect on the small scale of Taiwan’s software startups. He sees three challenges: appearing tiny alongside electronics companies with revenues in the tens or hundreds of billions, or even trillions; expanding effectively beyond Taiwan; and, crucially, increasing output per employee. He uses eLAND’s approximately 110 employees and roughly NT$1.66 million in revenue per employee as an illustrative comparison with the NT$4.63 million average employee salary announced by MediaTek in 2024. To attract excellent software engineers, AI companies need higher output per employee. He also includes the top ten average salaries for full-time non-management employees at listed companies in 2023, published by TWSE, with different years and measurement bases kept distinct.

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The post relays news that ChatGPT for Mac, for Team, Enterprise, and Edu users, added recording and cloud-drive integration. Starting a recording produces a live transcript that ChatGPT turns into summaries, key points, action items, and open questions. Unlike a recording bot that joins a meeting in tools such as Zoom or Notion, it listens through the device. Robin comments that application-layer AI startups must examine whether they are sufficiently differentiated. If the product is only a thin layer of added features, a technology giant could deliver the entire functionality in one update, perhaps better or more cheaply. Differentiation must be assessed from customers’ perspective: do they genuinely see a difference?

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The opening transcription from 科技早知道 discusses OpenAI’s GPT Store and related initiatives. If OpenAI builds every platform layer itself, it may reduce the space available to startups built on its technology. Robin’s personal impression is that the pursuit of technical breakthroughs and commercialization has taken OpenAI further from its founding intentions. Beyond the CEO’s direction, a company needs counterbalancing forces, especially when AI may materially affect society and human safety. A closed ecosystem that tries to do everything leaves less room for entrepreneurs within it.

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Robin argues that output per employee is central to pay differences: Taiwan’s technology sector generates much more per person than its service industries. Recalling his first job at an accounting firm, he notes that Taiwanese auditors faced additional regulatory work and documentation. An audit requiring three people abroad might take five or six in Taiwan, while clients resisted higher fees. He cites output per employee of roughly 10–20 million in export manufacturing, over 30 million at TSMC, and around 1 million in domestic services. Comparing starting compensation for TSMC engineers with a senior executive’s pay at a state-owned bank, he emphasizes the difference between serving global markets and serving only Taiwan.

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Robin recalls Intel’s US$300 billion market capitalization in 2000—six times TSMC’s and sixty times NVIDIA’s—versus roughly one-eighth of TSMC’s and less than one-thirtieth of NVIDIA’s at the time of writing. Seeking cash, Intel was considering selling Altera, acquired for US$16.7 billion in 2015. He reviews three CEOs: Krzanich’s rejection of first-generation ASML EUV and repeated 10-nanometer delays; Swan’s missed OpenAI investment and attempt to work with TSMC despite internal opposition; and Gelsinger’s IDM 2.0 and major factory expansion. He highlights vertical-integration risks while noting Intel’s design strength and US government support.

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Robin attributes Taiwan’s strong recent economic performance largely to US–China rivalry and US efforts to move away from Chinese supply chains. Taiwan filled the gap in semiconductors, ICT products, and servers, increasing exports to the United States and its trade surplus. But a US tariff calculation based on the trade-deficit ratio turned that advantage into a disadvantage, producing a higher rate than Japan’s or South Korea’s under the same formula. He sees even greater pressure on traditional industries that already lacked strong global competitiveness and now faced the additional tariff blow.

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While researching the IP industry, Robin notes Pop Mart’s market capitalization of around HK$285.5 billion, approximately NT$1.15 trillion—comparable to Quanta and above ASE, Chunghwa Telecom, and Cathay Financial. Its P/E is about 38, falling to an estimated 26–32 if growth is factored in. He leaves readers to judge the valuation but stresses that it makes real profits. He contrasts prestigious academic credentials with the achievements of Hon Hai’s Terry Gou and Pop Mart’s Wang Ning. Not yet forty, Wang was reportedly worth roughly US$20 billion and described as Henan’s richest person.

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Robin reviews Taiwan’s projected 2025 GDP growth of 7.41% and export growth around 32%, while traditional manufacturing, machining, and machine tools remain under tariff pressure. Since 2017, US–China rivalry, the pandemic, and AI have successively benefited Taiwan’s supply chain. Electronics, AI, and semiconductors gain directly; finance gains indirectly; many other people feel little benefit while prices and housing rise. He offers a personal view that rising housing costs keep well-paid technology workers striving, using ASPEED employees as an example. Academia Sinica’s 2026 growth forecast is lower at 3.7%, with much depending on continued AI demand.

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After Apple announced Vision Pro, Robin considered the options for small startups around the world that had spent years developing a single technology, such as eye tracking or gesture recognition. Startups with outstanding, clearly leading technology could become acquisition targets. Strong technology, a comprehensive patent portfolio, and a capable team could also attract a buyer seeking next-generation development talent. Others with ordinary technology but valuable patents could sell those while they still had value. Most laggards would gradually disappear. Headset makers lacking a developer ecosystem or price advantage against Meta Quest 3 and Apple Vision Pro would, he expected, mostly exit.

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Robin argues that semiconductors are Taiwan’s top priority and have put it in the global spotlight through choices about positioning, trade-offs, focus, and business models. With only 23 million people, Taiwan cannot do everything or win everywhere. China focuses resources on Olympic strengths such as diving and table tennis; South Korea knows its strengths in shooting and archery. Taiwan’s greater bottleneck is talent rather than capital. With fewer than 130,000 births a year, even if all those children entered semiconductor-related industries after graduating from college twenty years later, they might still not meet demand.

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Robin responds to the new digital affairs minister’s call for software companies to build products rather than take project work. Owning a product is preferable, but practical constraints matter. Trend Micro, founded in 1988, remains Taiwan’s most successful software company in his account, supported by Taiwan’s position as a computer-production powerhouse at the time. Semiconductors generate exceptional output, profits, and pay per employee and reach the world through supply-chain positions. Software lacks that structure and must actively reach global customers. Electronics companies have repeatedly spawned new ventures; software has yet to produce a similarly influential major company.

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In this short commentary, Robin considers Taiwan’s place in global industry. He links its manufacturing strengths with an education system emphasizing standardization. Rather than trying to become another Silicon Valley, he argues that Taiwan should build industrial capabilities Silicon Valley cannot do without. This is an industrial-positioning view, not causal research connecting education and manufacturing strength or a guarantee of future competitiveness.

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This 2026 column considers positioning for Taiwan’s frontier-technology startups through AI infrastructure, semiconductor supply chains, and US–Taiwan industrial complementarity. It advocates understanding end-market giants’ needs and pursuing international opportunities through existing supply chains, hardware–software integration, and strategic cooperation rather than doing everything alone.

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Founded in 1988, Trend Micro is presented as Taiwan’s most successful and international software company; thirty-six years later, it remains the name people mention. Robin argues Taiwan must value software: business owners, consumers, and creators need to pay for it. That culture is necessary for a truly digital island. The post links to reporting that Trend Micro was considering a sale.

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Robin argues that America’s strength owes much to sustained funding that attracts leading global talent to study, teach, and conduct frontier research. Funding freezes and interference with academic freedom threaten that foundation. Harvard president Alan Garber’s open letter said the university would not surrender its independence; more than US$2 billion in federal funding was subsequently frozen.

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Traditional automakers consider used-car values and keep raising prices; Tesla takes the opposite path, with other commentary describing a Model S reduction from US$140,000 to US$100,000. Robin compares the Model T, launched above US$800 in 1908, whose assembly-line production enabled falling prices and Henry Ford’s mass-ownership vision. Tesla resembles technology and internet businesses: direct sales and online performance updates. If it followed the post’s version of Moore’s law, performance would double or prices halve every eighteen months. Musk envisioned cars earning money as autonomous ride-hailing vehicles and storing rooftop solar energy to exploit time-of-use rates. Do not let current habits limit imagination.

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Robin argues that TSMC’s US$40–50 billion annual capital-spending scale is almost unaffordable for Intel. He considers talent, capital costs, focus, and business models: Taiwan concentrates technical talent at TSMC, while America’s best often go to Silicon Valley. He sees Samsung as the main competitor and EUV restrictions as a constraint on China.

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Reflecting on American divisions and a Facebook article, Robin questions whether greater freedom of expression and information have actually connected people. Algorithms can trap groups in echo chambers, showing only desired information and reinforcing beliefs, even shaping perception. He urges independent judgment and the ability to falsify claims when receiving algorithmic or AI-mediated information. A description of echo chambers explains repeated, distorted voices in closed environments. Seek varied experiences and perspectives while retaining your own reasoning.

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Robin contrasts his subjective picture of earlier founders such as Jobs and Gates focusing on products rather than politics with today’s close Silicon Valley–Washington ties: Thiel’s support, Musk’s funding, and OpenAI’s US$6.6 billion round led by Joshua Kushner’s Thrive Capital. He argues that geopolitics and antitrust pressures make what technology companies can do increasingly complicated.

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The post is tagged Wu Jun and Dedao. Only some concepts were located in third-party notes; the full original course was not obtained. The original post is retained in full; see the notes in the body for source details and limitations concerning figures and quotations.

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The original WeChat article and SpaceX material support SN5 completing a 150-meter test flight and landing in Texas on August 4, 2020, using a single Raptor engine. The original post is retained in full; see the notes in the body for source details and limitations concerning figures and quotations.

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This post summarizes Tim Urban’s observations of Musk: most people work from recipes—established practices—while Musk resembles a chef who redesigns things from first principles. Tesla illustrates the value of an outsider’s perspective and looking at a product afresh. Robin adds background on Urban and Musk at the end.

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Wan Weigang discusses DeepSeek R1 in January 2025: performance approaching o1, very low prices, and publicly available weights, and what these mean in contrast to OpenAI’s closed approach. Robin’s comments on open and closed approaches follow at the end. These views reflect January 2025.