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Risk of Recursive AI: Optimizing the Imperfect Objective

Recent discussions about slowing the development of artificial intelligence are often framed in dramatic terms: artificial general intelligence may become smarter than humans, recursively improve itself, and eventually escape human control. I find this framing somewhat misleading. Modern AI systems are not unconstrained intelligences. Transformer-based models operate within very real physical and computational limits: available compute, memory capacity and bandwidth, electrical power, training time, data quality, and the efficiency of optimization algorithms. Recursive self-improvement therefore does not imply that intelligence can increase without bound. The more interesting problem is different: What happens when an increasingly capable optimizer repeatedly optimizes an imperfect objective function without sufficiently independent external correction? We must consider AI as an optimization system. Consider a simplified learning system with parameters $\theta$ and objective function $...
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The Return of the Generalist in the Age of AI

Modern science has been built on specialization. This specialization was necessary and enormously productive. As scientific knowledge expanded during the twentieth century, no individual could master everything. Mathematics, physics, engineering, biology, and medicine each divided into increasingly sophisticated subfields. By the latter part of the twentieth century, becoming a serious researcher often meant spending many years mastering a progressively narrower area of knowledge. This system produced extraordinary scientific achievements. But it also produced fragmentation. In the early twentieth century, the boundaries between scientific disciplines were often more permeable than they are today. It was still possible for an individual scientist to move substantially among mathematics, physics, engineering, and experimental science. As each field became deeper and more technically demanding, however, this became increasingly difficult. By the 1970s and afterward, specialization had be...
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Healthcare AI Should Make Medicine Cheaper and More Accessible, Not Just More Advanced

When I look at recent medical research from leading American institutions, I am often impressed by the intelligence, dedication, and technical sophistication of the researchers. Yet I have also felt increasingly uneasy about the direction in which our research system pushes these talented people. Too much intellectual energy is devoted to making already advanced medicine even more advanced, while comparatively little is devoted to making adequate medicine dramatically cheaper, simpler, and more accessible.  This is not an argument against frontier medical research. We absolutely need high-field MRI, photon-counting CT, advanced PET imaging, molecular diagnostics, precision oncology, and sophisticated medical AI. A small number of outstanding laboratories should continue pushing these technologies to their limits. Breakthrough research is inherently uncertain, and government support is essential because private markets will often underinvest in discoveries whose benefits may lie de...
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When Research Prestige Becomes a Business Liability

The extraordinary expansion of artificial intelligence is creating an unusual economic environment. OpenAI, Anthropic, Google, and other frontier AI developers are competing in a market where slowing investment may itself be more dangerous than investing too aggressively. As long as improvements in models, inference capacity, and agentic systems continue to generate meaningful productivity gains, each company has a strong incentive to secure as much computing capacity as possible. This creates something resembling a survival game. This blog post argues that innovation is not enough, being first is not enough, and scientific excellence is not enough. In a rapidly changing technological environment, none of these advantages alone guarantees survival. What may matter most is an organization’s ability to repeatedly adapt and reorganize itself as the nature of competition changes. Fundamental research is indispensable to technological progress. However, when success in fundamental research ...
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Agentic AI May Be the Next Major Driver of the Memory Supercycle

The current artificial intelligence boom, driven by platforms such as ChatGPT, Gemini, and Claude, is associated with rapidly growing demand for GPUs and high-bandwidth memory. But this may represent only one stage of a broader transformation in AI adoption. Over time, agentic AI could become an increasingly important driver. Unlike conventional chatbots that primarily respond to prompts, agentic AI systems can perform multi-step tasks, interact with software, retrieve information, make intermediate decisions, maintain working context, and coordinate with other agents.  Before proceeding, I would like to note that I wrote this blog post with the assistance of ChatGPT.  If agentic AI is adopted at scale across enterprises and other organizations, its infrastructure requirements could extend well beyond those of the current chatbot-centric model. For the semiconductor industry, this could broaden AI-related memory demand beyond HBM to server DRAM, enterprise SSDs, NAND flash, CX...
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Nonlinear dynamics induced by single-stock leveraged ETF

During July 2026, the Korean stock market experienced an unprecedented series of circuit-breaker and sidecar activations. Although global trade tensions and macroeconomic uncertainty initiated the market decline, the subsequent price movements were amplified by endogenous market mechanisms, resulting in substantial deviations of stock prices from their underlying fundamental values. The phenomenon was particularly pronounced in Samsung Electronics and SK Hynix. Despite reporting record earnings and maintaining robust business fundamentals, both companies experienced sharp price declines. Unlike most developed equity markets, Korea combines two distinctive structural characteristics: Samsung Electronics and SK Hynix account for a substantial fraction of the KOSPI capitalization, while both are actively traded through single-stock leveraged ETFs. Because these products concentrate leveraged exposure on individual stocks rather than diversified portfolios, their mandatory daily rebalancin...
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Why Correct Predictions Are Not Enough: Survival Comes First

Recently, I came across a news headline that caught my attention: “Why Did the 25-Year-Old AI Genius of Wall Street, Leopold Aschenbrenner, Fail?” The headline prompted me to write this blog post because I have seen friends go through somewhat similar experiences in their own investing. I should also mention that I am not a professional investor. What follows is simply my personal attempt to think about investing through the lens of mathematics, with substantial assistance from ChatGPT. On July 30, 2026, Leopold Aschenbrenner, the 25-year-old founder of the San Francisco-based hedge fund *Situational Awareness*, sent a letter to his investors. A former OpenAI researcher, Aschenbrenner graduated from Columbia University in 2021 as valedictorian with a B.A. in economics, mathematics, and statistics. In the letter, he acknowledged that his fund had lost **67% of its value in July alone**. For someone who had been widely regarded as an exceptionally talented young thinker in AI and investi...
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