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With Meta, Google, Amazon, Microsoft, Apple, Tesla, and other tech giants freezing headcount and reducing recruitment, Robin expects startups to tighten their belts too and Silicon Valley’s freezes and layoffs to continue. His personal guess is at least twelve to twenty-four months, with layoffs possibly exceeding 20%; these are guesses, not statistics. He speculates that three to five years of product-led growth have left marketing and sales leaner than at traditional sales-led software companies. SaaS tools also help keep HR lean, leaving more bloated engineering and product departments to be cut. He warns Taiwanese startups to be alert to the coming “ice storm.”

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The post explains that AIGC stands for AI-generated content: artificial intelligence can automatically generate news articles, interactive conversations, images, speech, music, code, legal documents, financial statements, and more. It ends by suggesting that when AI becomes proficient in playing instruments, board games, calligraphy, painting, poetry, prose, music, and office work—and can also drive itself—humanity must consider its own value at a more philosophical level. The source and originality remain unconfirmed. This is a conceptual explanation from December 2022.

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The post draws on Harari’s argument in Sapiens: humans can build large-scale social organizations because they have the extraordinary ability to imagine things that do not exist. Shared imagination makes cooperation at scale possible. Robin’s personal view is that AI still lacks imagination, including the ability to derive new knowledge and generate new insights. It functions more like a system that assembles and presents large amounts of existing information, and it may even lack the ability to make value judgments. For example, when asked whether vaccines are beneficial or harmful, AI can gather much of the information online and draw a conclusion, but that conclusion still rests on existing scientific data available online. This is a personal assessment from December 2022; AI’s capabilities have changed since then.

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The post retells an analogy: when an apple hits Newton, he abstracts a theory of universal gravitation; when it hits ChatGPT, it merely explains what happens when an apple hits you, how to avoid it, and the benefits of eating apples. The ability to connect seemingly unrelated things and develop a systematic theory remains, at that time, ChatGPT’s ceiling. Robin adds that a Newton-level genius may appear only once among millions of people. Most people’s routine work still faces considerable risk in the foreseeable future. If someone else can do your job just as well, the risk is high. In his example, A actively embraces ChatGPT and may become so much more productive that A replaces B, who passively waits.

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Robin notes that Taiwanese companies that reach a substantial scale generally do business worldwide. In electronics, apparel, footwear, bicycles, and similar industries, a company can secure a place in a long supply chain and reach the world through downstream global brands. Early Taiwanese software could also ride the wave of an industry in which Taiwanese manufacturers made nine out of every ten computers: shipping with those computers provided a degree of global reach. But once phones replaced computers and software began reaching users through the cloud and app stores, Taiwanese startups had to find their own internationalization strategies. The Taiwanese market alone presents too low a ceiling. Robin hopes several Taiwanese software companies can play a role like Acer, Asus, Giant, or Merida, taking smaller startups into international markets alongside them.

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The post argues for starting with a change in how we teach. Traditional large classes can become one-on-one, tutor-style question-and-answer exchanges in the AI era. Taiwanese students, who have not been accustomed to asking questions, must now actively develop and organize their own questions. In the past, the worry was that a teacher could not answer. Now the worry is that we cannot formulate questions well: asking the right question matters more than being given an answer directly. If an explanation is unclear, students can keep asking, request a different explanation, ask for language an elementary school student can understand, or seek more depth and examples. The author argues that education in this new era needs to be redesigned.

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Robin sees a critical moment for transforming education: Taiwanese children spend their days on workbooks and complete more than ten thousand test papers, while American children work on hundreds of projects from an early age, developing teamwork and problem-solving skills. The excerpt that follows comes from Wan Weigang’s column. It argues that education needs to change even without the AI wave; AI simply makes the problems more visible. In programming, for example, the question should not be which language you studied or how many facts you mastered, but which projects you have completed. We need to move from a “course mindset” to a “project mindset.” The excerpt also argues that GPT helps people learn faster rather than giving them a reason not to learn.

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The post first quotes Wang Xing: a company’s starting point is not watching competitors but focusing on customers and creating value for them. A company can exist only if customers are willing to pay. Robin then adds that the internet has produced many new services, and now the AI moment has arrived. He expects many existing workflows to be disrupted or reinvented. Services that once required hundreds or thousands of people may become possible with a few dozen or ten-plus people. They are worth reimagining and doing over again. This is Robin’s expectation as of May 2023.

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Robin notes that NVIDIA’s Taiwanese partners include both major hardware companies and nascent software startups. Amid the AI wave, the transition from old to new, and hardware–software integration, he believes Taiwan can play more critical roles. He cites NVIDIA’s market capitalization that day as approximately US$783.4 billion, the fifth largest in the U.S. market; these figures are as of May 19, 2023, and have not been independently verified. He hopes this will help Taiwan keep upgrading and transforming in AI. He ends with a retrospective quip: investing several years earlier, when the market capitalization was around US$20 billion, would have produced roughly a fortyfold return over seven years. This is not investment advice.

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The post reprints a Dedao Headlines piece explaining why some powerful technology companies have so few people. It uses Midjourney, with approximately eleven employees, as an example; the figures are those stated in the reprint as of June 2023 and have not been independently verified. The analysis offers three reasons: low marginal costs in technology product development, mature infrastructure that removes the need to reinvent the wheel, and the 10x engineer effect. An additional section discusses team management at the “critical point of a phase transition.” Robin’s closing comment is that if revenue growth is highly correlated with headcount growth, the business is fundamentally a traditional, labor-driven enterprise rather than a technology company.

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The post reprints a Wan Weigang article, tagged #WanWeigangEliteDailyLessons5, arguing that entrepreneurs’ most important decisions rest on intuition and imagination rather than scientific rationality—for example, building rockets or betting on large language models. It cites Zhang Weiying’s Chongxin Lijie Qiyejia Jingshen (“Rethinking Entrepreneurship”): someone else saying you cannot succeed does not mean you actually cannot. The author says execution requires science and reason, but an entrepreneur’s role is to initiate a vision beyond normal operations and discover new possibilities. The world contains not only quantifiable risk but also unquantifiable uncertainty—Knightian uncertainty. The entrepreneur’s task is to create uncertainty rather than adapt to risk.

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The excerpt describes the cycle of enthusiasm for new technology: it is initially hyped, expectations then fall to a trough, but technology that genuinely advances digitalization and intelligence can eventually surpass those original expectations. It describes AI as overheated at the time and argues that extraordinary entrepreneurial revolutions often arise outside the consensus as markets cycle between excessive enthusiasm and excessive pessimism. Robin adds that investors can wait and see, but founders cannot remain on the sidelines. They must keep moving and adjust flexibly along the way. He also urges caution about market consensus: good investors and entrepreneurs need independent views.

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The post presents three excerpts, each followed by a brief comment from Robin. First: a business should advance trends rather than fight them, understand how its category is developing, and position itself in line with long-term market trends. Robin says no one can fight a trend, not even Steve Jobs. Second: sustained investment and resource accumulation in a clear direction create strategic compounding; do not scatter resources lightly. Robin says to keep focusing on what matters most. Third: entrepreneurs should spend their time on work that compounds. Robin says entrepreneurship means doing things whose value can accumulate. The excerpted views are not Robin’s original writing.

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Robin asks whether the AI wave calls for more engineers versed in mathematics and physics, such as Elon Musk, or more people who bridge technology and the humanities, such as Steve Jobs. The following excerpt says Jobs more than once closed a product presentation with a slide showing the intersection of technology and the humanities. He said Apple could create products such as the iPad because it had always tried to stand at that intersection. The excerpt attributes the idea originally to Polaroid founder Edwin Land; this retelling has not been independently verified. Robin poses the question without reaching a conclusion.

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Robin’s personal impression is that Taiwan has unexpectedly become the biggest beneficiary of spillovers from the U.S.-led AI boom. Semiconductors, passive components, thermal management, servers, PCs, and even power infrastructure have all been revitalized by AI. He believes Taiwan remains exceptionally strong in hardware and holds a distinctive industrial and supply-chain position as the United States seeks to contain China. It would be extremely difficult for other countries to replicate companies such as Hon Hai, Quanta, Wistron, and Inventec. Taiwanese AI software startups, however, still have work to do.

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Returning to his post a few days earlier, Robin’s personal impression is that Taiwan receives the most pronounced AI spillover benefits, measured as additional output attributable to AI divided by population. Yet those benefits remain concentrated in hardware, including semiconductors, servers, thermal-management components, and power infrastructure. AI software and applications have not taken off. Outside the United States, however, they generally have not made an obvious revenue contribution in other countries either. Robin emphasizes that AI software startups need to focus on revenue, its growth trend, and output per employee to attract extremely expensive AI software engineers and scientists.

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Robin asks whether another group of AI startups may soon have to “change direction.” Some, for example, are building AI customer-service tools, but major companies may directly absorb most of those functions. More human customer-service staff may also need to change careers over the next year or two. This is a personal observation and conjecture from May 2024, intended to remind AI startups to think carefully about their position and sustainable differentiation.

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The post lists observations from June 3, 2024: the market capitalizations of the three largest U.S.-listed companies were within an 8.6% range and their ranking could change at any moment; four companies had surpassed US$2 trillion; and three of the ten largest were semiconductor-related. Robin concludes that Taiwan, a small country with a small population, cannot pursue everything. Focusing resources on semiconductors and related areas can still have a profound global impact. The only foreign company among the top ten came from Taiwan in Asia. The figures refer to the date stated in the post; their source remains unconfirmed.

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The post argues that the future is about computers understanding your intentions rather than your spending considerable effort learning how to use them. Human–computer interaction needs to be reinvented to become more natural, simple, and intuitive. When the barriers to AI use fall sharply, people need not memorize a collection of prompts: they can converse naturally with AI. This can give rise to more innovation, use cases, and commercial value. The post points to shifts from mainframes to personal computers, graphical interfaces, touch and voice input, widespread internet access, and smart handheld devices. Each transformation carries enormous business opportunities that we must perceive, experience, and imagine.

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Looking at the Gartner Hype Cycle, Robin believes generative AI is already at a peak. The post quotes Bill Gates as saying that we always overestimate the changes that may occur in the next two years and underestimate those that may occur in the next ten; the quotation’s source remains unconfirmed. Robin’s point is that the real challenge is moving from technology enthusiasts and early adopters to widespread use among the early majority. If everyone around you is using AI and cannot do without it, that indicates a successful transition toward the early majority. This is his personal assessment from June 2024.

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The post begins with a quotation: a genuinely powerful advantage requires talent and years of sustained deliberate practice. Once established, it creates a vast gap between a person and ordinary performers. Robin then says Taiwan already has a substantial systemic advantage in semiconductor wafers. The question is whether to keep investing resources to strengthen it or diversify because the concentration is too high. In his personal view, Taiwan’s most important resource remains high-quality talent. Yet with approximately 130,000 births a year, as stated in the post, even assigning all of those children to semiconductors and electronics in twenty years might not be enough. Excessive concentration is also risky, so Taiwan must identify its true core strengths rather than try to do everything.

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Robin mentions the launch of Tesla’s Optimus Gen 2. Just as AI application startups worry about OpenAI announcing another breakthrough application, robotics companies must keep close watch on Tesla’s progress. He asks whether, in ten years, declining birth rates will seem less important if AI takes on a large amount of work and robots also enter labor and service roles at scale. He believes the real issue returns to philosophy: the meaning of being human and the value of our existence. These are speculative questions about the future, not a forecast.

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Robin notes that podcasts have been popular in Taiwan for several years. His personal impression is that, just as Netflix’s rise pushed traditional cable television companies around the world into retreat, on-demand podcasts could shake up traditional radio. They could also create a larger, entirely new business model for audio creators, including audiobook content. The future still offers much to imagine. This is a personal impression and analogy, not a market statistic.

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Robin summarizes four points from his talk the previous day. First, focus the business model: you are unlikely to make money from everything. Second, users may not feel much difference between AI recognition rates of 93%, 94%, and 95%, and each company excels in different situations. Rather than pursuing another 1–2% improvement, address the most pressing points in users’ workflows. Third, technology quality is one thing; sales channels and partners are another. Sometimes the problem is that a finished product does not sell. Fourth, consider major companies’ technology roadmaps. Using Apple’s addition of medical-device functions to wearables as an example—as of September 2024—he urges startups to identify the space left for them when major players target the mass market.

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Robin notes that AI technology is advancing rapidly. Major companies keep releasing new large models, and benchmark systems are used to assess their accuracy. His personal view is that scores of 93, 95, or 96 may make little difference to end users’ experience. Rather than spending time and resources gaining another benchmark point, companies should deploy the technology in real use cases and make the entire user experience smoother. This is his personal opinion, not a benchmark research finding.

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In September 2024, Robin suggested looking at industry and gross margin when discussing startup output per employee, and using annual gross profit divided by total employees to assess individual contribution. He mentioned approximately 15 million in gross profit per employee at TSMC and an intuitive startup threshold of approximately 1–1.5 million, but the original post did not specify currency, calculation year, or the headcount basis. TSMC’s consolidated gross profit and global workforce in its 2023 annual report produce a similar order of magnitude. That does not establish Robin’s original calculation, nor is it an officially reported gross-profit-per-employee metric. His suggestions on automation, AI, and outsourcing are personal views, not universal industry standards.

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Revisiting the ten largest U.S.-listed companies by market capitalization in December 2024, Robin takes a different perspective and counts Apple as a hardware business. Five companies sell or manufacture hardware products: Apple, NVIDIA, Tesla, TSMC, and Broadcom. Four are software or internet businesses: Microsoft, Amazon, Google, and Meta. One is a financial investment company. “Software is eating the world” may be true, he says, but hardware’s importance seems to have risen rapidly over the previous year or two, and Taiwan’s global hardware competitiveness appears much stronger than its software competitiveness. He also notes a biotechnology company in eleventh place and describes this as the period with the most U.S.-listed companies ever valued above US$1 trillion—nine in total. Rankings and counts refer to the date of the post and have not been independently verified.

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As A Chip Odyssey attracts attention, Robin notes that it primarily depicts the development of Taiwan’s semiconductor industry. Taiwan has another major industry: electronics and information and communications technology (ICT). He recalls San Ai Electronics, founded in 1972 to make audio equipment, followed by electronic calculators and the emergence of major groups. Stan Shih’s founding of Acer in 1976 also led to multiple corporate groups. ICT made Taiwan a major producer of PCs, notebooks, and servers, with revenue and output that stand alongside semiconductors. He urges his generation to carry the legacy forward rather than continue living off achievements from forty or fifty years ago, and to create a new era for the next generation. These company histories are recollections and have not been checked individually.

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The post reprints three ideas from Liu Run. First, AI replaces tasks rather than occupations: identify what AI can do and what only you can do. Second, the gray-collar era has arrived, favoring people who understand technology and can do hands-on work. Third, hard skills become outdated, while soft skills such as communication, judgment, and problem-solving form a moat. The piece concludes that we should not compete with AI at what it does best. When getting things right is no longer difficult, setting direction and taking responsibility become the scarcest resources. Robin adds the ability to ask good questions, organize and clearly express ideas, simplify complexity, think abstractly, use imagination, set an organization’s direction and boundaries, collaborate, and take ultimate responsibility.

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After listening to a technology program, Robin reflects that China’s AI models still trail those in the United States and are trying various ways to break through restrictions. He mentions the program’s discussion of distillation attacks and related claims, which have not been independently verified. He observes that Chinese model developers are concentrating on open source. Even in the U.S., many developers use these models in open-source communities to build products, having been warned not to base a business model on a major provider’s models with only a thin wrapper that can easily be replaced. At the time, U.S. model providers mostly favored closed approaches, while Chinese providers were building visibility through openness. Whether the latter would later turn toward closed models remained unknown.

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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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The post presents several short arguments about strategy. Strategy means being explicit about what to do and what not to do; focus requires both choice and sacrifice. If you cannot reach the top three in a niche, do not rush into a second business. First take the original business to its fullest potential, then turn methods for analyzing customers, building teams, and establishing rules into organizational capabilities that can be applied to new businesses. Even a market worth hundreds of billions is irrelevant to you if your organizational capabilities cannot support it.

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After listening to Keji Daodu, Robin argues that a startup should focus on the problem it seeks to solve and the customer’s fundamental needs, rather than watch existing competitors. Tesla did not focus on Toyota and Volkswagen, and SpaceX redesigned rockets. These examples illustrate his view that concentrating on competitors would merely produce a faster version of a traditional product. After the divider, an external strategy passage discusses dozens of agile small challengers.