Strategic Tech and Market Signals
From Windows RT to RTX Spark: Has Agentic AI Changed the Conditions for Windows on Arm to Succeed?
Executive Summary Windows on Arm has drawn attention several times, yet it has struggled to achieve a meaningful breakthrough. The challenge is not limited to processor performance. It also reflects decades of accumulated compatibility requirements across Windows software, drivers, and enterprise systems. The Chromebook experience further shows that a lighter compatibility burden does not automatically turn Arm’s power-efficiency advantage into market share. Consumers and businesses do not buy an architecture alone. They evaluate the full
Beyond the Traditional Hardware Framework: AI Infrastructure Is Forming a New System Cycle
Executive Summary This AI infrastructure cycle may not be fully understood through the traditional framework of technology hardware cycles. First, although the market keeps discussing an AI bubble, these early bubble warnings may actually make suppliers more disciplined about capital spending and delay the point at which supply becomes excessive. Second, KV cache is changing the architecture of AI infrastructure. AI infrastructure is no longer only a question of supply and demand for individual hardware
The Market Trusts Buildable AI, But Still Waits for AI That Customers Will Pay For
Executive Summary Investment markets are applying two different standards of evidence to AI. The market has been willing to believe in AI infrastructure because GPUs, data centers, AI servers, optical communications, liquid cooling, power equipment, and supply chain orders can be built, measured, and reflected in financial results. But when the discussion shifts to SaaS and AI applications, the market asks for clearer proof of commercialization, including enterprise willingness to pay, user habits, workflow change,
Google and Anthropic’s Competition: Two Different Paths in the AI Era
Executive Summary Google and Anthropic are not the most obvious rivals, but that is precisely why the comparison is worth paying attention to. Google represents a full platform path, with models, cloud infrastructure, enterprise tools, and global scale, and seeks to absorb AI into its existing platforms and enterprise systems. Anthropic represents a more focused path, seeking to build a long term position across multiple platforms through model capabilities, enterprise trust, and clear positioning. The
Why Analyzing AI Forced Me to Reclaim the Skills of a Financial Analyst
AI has forced me to rethink not only how I read companies, but also how I read markets. What began as industry analysis gradually led me back to skills I once used as a financial analyst. This essay is a reflection on why that happened. I never expected that one day I would write an essay like this. For me, industry analysis has always had a certain kind of
Why New AI Demand Still Often Flows to the NVIDIA Ecosystem
Executive Summary The AI compute market is becoming increasingly diverse. Large cloud providers continue to push forward with in-house ASIC and XPU development, and the number of alternatives to NVIDIA keeps growing. In theory, new AI demand should become more evenly distributed across different architectures, rather than continuing to concentrate in the NVIDIA ecosystem. But when several recent signals are viewed together, the key question may not simply be who has compute. It may be
Could AI’s Next Growth Phase Be Faster Than Expected?
Executive Summary A recent remark by Groq founder Jonathan Ross raises an important question. If models begin to improve the quality of their own learning signals, then the AI growth logic we have become familiar with may no longer follow the same path of diminishing returns. This article does not ask whether Ross’s claim should be accepted at face value. It asks whether the idea behind it is already supported by a set of meaningful
The Linear Narrative Around AI Memory Demand May Be Starting to Show Small Cracks
Executive Summary In current discussions around AI infrastructure, the market broadly assumes that memory demand will continue rising steadily as models scale, inference workloads expand, and HBM and DRAM remain under supply pressure. This narrative is grounded in real conditions, which is also why it appears especially durable. But once the focus shifts from demand itself to system design, the picture becomes less straightforward. As memory supply, cost, and capacity allocation increasingly become real constraints,
After the Groq Move, NVIDIA’s Moat May Be Deeper Than It Appears
Executive Summary At first glance, NVIDIA’s move to incorporate the Groq-based NVIDIA Groq 3 LPX into the Vera Rubin platform may look like a new approach to inference workload allocation. But the real focus of this article is not the technical detail itself. It is whether this move suggests that NVIDIA’s moat may be deeper than it previously appeared. The argument here is that NVIDIA’s competitive strength may not rest only on chip performance, the
The Expansion Logic of AI Infrastructure Is Changing
Executive Summary Several recent signals that appear unrelated at first glance may in fact point to a shift in how decisions around AI infrastructure are being made. Adjustments to the expansion pace of the Abilene data center by OpenAI and Oracle, together with Meta’s description of its in-house AI chip roadmap for MTIA, suggest that companies are facing the same underlying question. As model development, chip generations, and infrastructure construction cycles become increasingly out of