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From Bioimpedance to Light: Reflections on the 2026 Nobel Prize in Physiology or Medicine

The 2026 Nobel Prize in Physiology or Medicine, awarded to Peter Hegemann, Georg Nagel, and Karl Deisseroth for discoveries concerning light-gated ion channels and optogenetics, brought me back to a research problem I worked on about 20 years ago. Our work was part of a broader effort to detect neural activity through changes in bioimpedance using electrical impedance tomography (EIT). The award reminded me that the opening of an ion channel can be approached both as something to measure and as something to control. Around 25 years ago, a clinical neurophysiologist at University College London explained the physical basis of this approach to me. When a neuron becomes active, ion channels open and its membrane conductance changes. The hope was that the resulting change in tissue impedance would allow researchers to follow the propagation of an action potential. A central challenge was obtaining reliable measurements of small, brief changes within signals influenced by many cells, extrac...
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The Courage to Abandon a Beautiful Idea

One of the most difficult skills in research is not learning how to develop an idea, but knowing when to abandon one. Scientific training naturally emphasizes persistence. Researchers are taught not to give up when experiments fail, reviewers disagree, or evidence appears inconsistent with an initial hypothesis. This persistence is essential. Most important discoveries would never have been made without it. Yet persistence has a less discussed counterpart. What happens when the idea itself is wrong? The problem becomes particularly difficult when a research program has developed over many years. A theory or methodology gradually accumulates papers, data, techniques, grants, students, collaborations, and professional recognition. What began as a hypothesis becomes an intellectual ecosystem. At that point, abandoning the idea is no longer a simple scientific decision. There are also powerful intellectual mechanisms that favor continuation. When observations conflict with a theory, it is ...
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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

Before beginning, I want to clarify what I mean by a “generalist.” I do not mean someone with superficial knowledge of many fields, but someone capable of handling a substantial range of work across multiple domains, though not with the depth of a specialist in each. This kind of generalist may become particularly valuable in aging societies, where experienced older people represent a large reservoir of accumulated knowledge and judgment that AI can help put to productive use. 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 extraor...
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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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