AI

** In a world of infinite content and scalable intelligence, the scarcest resource may no longer be intelligence itself — but the ability to truly see another human being, and to be seen in return.** By Sinclair Huang I walked into a bookstore — and realised something uncomfortable A few days ago, I walked into a bookstore. Surrounded by shelves of books — many of them thoughtful, deeply human, and difficult to summarise — I had a quiet realisation: ...

Work, ownership, and the new architecture of economic security By Po-Sung(Sinclair) Huang For decades, people believed that working hard was enough. In the AI era, that assumption is quietly breaking. Not because work disappears, but because ownership matters more than ever. In the AI era, relying on labour income alone is no longer a neutral choice. It is an active form of risk. That sentence may sound harsh. But it captures a structural shift that many people can already feel, even if they do not yet have the language for it. They feel it in markets. In career anxiety. In the fear of being left behind by a technology wave they did not ask for, do not fully control, and may not directly benefit from. ...

From dBase and enterprise systems to the internet revolution and generative AI, I have come to see AI not as a sudden break, but as the latest step in a long slope of automation now reaching human cognitive work itself.* By Po-Sung(Sinclair) Huang I did not decide to write this essay because AI suddenly became fashionable. It came out of two images that collided in my mind. One was a group of younger people trying to imagine new ventures built around AI. The other was the story of a father facing a rare disease so obscure it seemed to leave almost no path forward, and yet continuing, late into the night, to search for a way through with the help of computation, search, and structured reasoning. ...

From compute bottlenecks to industrial consequences — where value may actually concentrate through 2030 Series: AI Compute Supply Chain | Part 5 of 5 Author:* Po-Sung(Sinclair) Huang |For the past four articles in this series, I have written about CoWoS, HBM, ABF substrates, SEC filings, and the fault lines that could eventually crack today’s moats. On the surface, that may look like a semiconductor series. It is not. What these articles really reveal is something larger: AI is no longer just a software story, and no longer just a model race. It is becoming an industrial system — one that depends on power, cooling, capital expenditure, advanced packaging, memory bandwidth, substrate materials, qualification cycles, and the physical discipline of manufacturing scale. ...

A new study in Nature Machine Intelligence suggests AI may do more than summarise knowledge or accelerate discovery. It may begin to shape which scientific questions are noticed first. When AI Starts Predicting the Next Scientific QuestionWe have grown used to thinking about AI as an answer machine. It summarises papers, organises data, generates hypotheses, and accelerates analysis. In that familiar picture, AI helps scientists move faster toward results that humans still define. ...

Why is everyone suddenly talking about CoWoS, HBM, and ABF whenever AI, NVIDIA, or AI servers come up? Many people know they are important, but still get stuck the first time they run into these terms. This essay is a plain‑language walkthrough of what they actually are, why they are always mentioned together, and how they map onto Taiwan’s role in the global AI supply chain. When Google, Amazon, and Tesla are all designing their own chips, is Taiwan’s manufacturing ecosystem still structurally important — or just enjoying a temporary window? ...

Every layer that looks solved hides another constraint beneath it. §1 The Illusion of Infinite Compute The headlines say NVIDIA is winning. The hyperscalers are spending. The models are getting bigger. But the real question is not where demand is going. It is where compute, physically, can still be built fast enough to meet it. The harder answer requires tracing the full physical stack — from silicon wafer, through memory stack, through packaging interposer, through substrate material — and asking at each layer: can this actually scale at the speed the demand curve requires? ...

AI in drug discovery is often framed as a speed story. Faster screening, faster structure prediction, faster candidate generation. But speed is only the surface. What AI really changes is the way search space is organized. In traditional drug discovery, much of the challenge lies not only in testing compounds, but in deciding where to look. AI expands the ability to navigate vast biological and chemical spaces, but it does not eliminate the underlying uncertainty of biology itself. ...

For years, digital strategy was built around owning traffic and bringing users back to a company website. That logic may be weakening. As AI systems become better at summarizing, filtering, comparing, and presenting options directly to users, the key source of value may shift from website ownership to interface control. In other words, the winner may not always be the company with the best homepage, but the company, platform, or system that controls how customer intent is interpreted and how choices are presented. ...

In every wave of commerce, people tend to focus on tools first. They talk about better platforms, smoother interfaces, lower friction, faster conversion, and now AI-driven recommendation systems. But the first principle of commerce has not changed. Trust comes before efficiency. Before a customer asks whether a platform is convenient, they ask whether the product is real, whether the seller is credible, and whether the transaction can be completed safely. This was true in early e-commerce, and it remains true in the AI era. ...