Global Business Dynamics
In the AI Era, the Market Is Reassessing Software Companies
Executive Summary For some time, software companies have continued to report solid, and sometimes better than expected, earnings results. Yet the market response has remained relatively cautious. This gap may not reflect a problem with company performance. It may reflect a broader reassessment of software companies in the AI era. In the past, software companies could often earn higher valuations through subscription models and predictable growth. In the AI era, the market is beginning to
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 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
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
A Second Path Beyond the GPU? Architectural Thinking Behind NVIDIA’s Licensing Agreement with Groq
Executive Summary NVIDIA’s licensing agreement with Groq is worth watching not only because the technology itself is extreme, but because it may signal that AI compute architecture is being reconsidered. Even after GPUs have become the dominant platform for AI training and inference, NVIDIA is still willing to engage seriously with an execution model that runs almost counter to the mainstream path. That suggests the demands of the inference era may be making determinism important
CPU as an AI Pillar, Is Arm Approaching a Structural Inflection?
Note (March 2026): I wrote this piece before Arm officially unveiled its own data center CPU. That does not make the original argument irrelevant, but it does change the context in an important way. I am keeping the article largely as it is because the framework still helps explain what to watch. What has changed is that some of the questions discussed here are no longer purely hypothetical. They can now be read
When Grace CPU Reaches Its First Large-Scale Deployment: This Is Not Just a CPU Story but Also a Shift in Data Center Structure
Executive Summary The first large-scale deployment of the Grace CPU may appear, at the surface level, to be a routine update on product and partnership progress. Within a broader industry context, however, this development may carry structural implications that extend beyond a single product milestone. This article examines the signals embedded in Grace CPU’s large-scale deployment from the perspectives of market positioning, data center architectural evolution, and hyperscaler strategy. These signals include NVIDIA’s changing role
AI Is Reshaping the Cost Structure of the Software Industry
Executive Summary From Microsoft to Google, senior executives have increasingly centered their earnings discussions on token efficiency, inference costs, and overall system utilization. This shift in language points to a deeper structural change. As software usage itself begins to incur meaningful costs, the long-held SaaS assumption that higher usage naturally leads to higher margins no longer holds universally. For software companies that lack scale, bargaining power over compute resources, or structural cost advantages, heavy users