Thoughts on ZetaChain and Anuma AI moving toward an omni-modal AI infrastructure
Recently, observing ZetaChain’s strategic direction and seeing Andrew Lim of Anuma AI Korea (@ZetaChain_KR) participate in the Agent Execution Frontier: Seoul 2026 panel session, I found myself reflecting on much broader implications—not merely about a single chain or service, but about how AI will ultimately connect with and execute for users in the future.
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Particularly compelling was Anuma AI’s approach: the ability to compare multiple AI models within a single platform, while accumulating and leveraging data through a unified memory layer. This isn’t simply a passive aggregation of models—it’s a platform that actively creates its own workspace.
Anuma currently offers free access to a set amount of usage, providing an excellent environment to directly compare various models. More importantly, it enables users to retain and manage responses and context across multiple models based on a single memory layer. Anyone who has experimented with multiple AI tools knows the real frustration isn’t model performance itself, but the fragmentation of records across platforms and the repetitive consumption of tokens. In this regard, Anuma has significantly improved its UI and user flow—and it shows strong potential for further advancement.
Anuma’s token system allows users to lock up ZETA tokens in exchange for Anuma AI tokens to access AI models, while also earning an annual interest rate of 12%, making the approach feel less burdensome.
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Looking at the broader AI ecosystem, each platform competes based on distinct strengths: Google @antigravity, Anthropic @claudeai, Manus AI @ManusAI, Openclaw, Hermes Agent, and many others continue to emerge. Users increasingly switch between models depending on their needs. The problem is that as this fragmentation grows, so too does the demand for computational resources, usage volume, and overall cost. Although unit performance costs are declining with technological progress, demand is rising even faster—meaning the perceived cost burden hasn’t disappeared entirely. Efficiency improves, but total usage expands even more.
At this point, hardware bottlenecks become impossible to ignore. Recently, I set up two high-performance computers—one with AMD Ryzen and NVIDIA RTX, the other with AMD Ryzen and AMD Radeon—and used them for several weeks. What became clear is that one of the core challenges in high-performance computing is heat generation, power consumption, and the hyperscale infrastructure required to manage them.
High-performance GPUs inevitably face worsening thermal issues as performance competition intensifies. Even in the consumer GPU market, recent models show design compromises—such as cut-down chips—to address heat and power concerns. AI data center GPUs, meanwhile, rely on aggressive cooling solutions like immersion cooling with HBM architectures or enhanced active cooling systems to sustain performance. Ultimately, while both are “AI computation,” the methods used by personal devices versus data centers are diverging sharply.
(Something between data centers and personal AI assistants)
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NVIDIA’s next-generation CPUs and GPUs will not be immune to these challenges. No matter how sophisticated the design, hardware bottlenecks will likely eventually constrain the pace of software advancement. And this bottleneck won’t be confined to semiconductor engineering alone—it will connect to data centers, power infrastructure, cooling systems, optical cables, supply chain components (SBU), and the financial investment markets that underpin them all. From this perspective, AI’s growth cannot be explained by mere technological optimism; at some point, structural limits, investment credit pressure, and supply chain strain will inevitably surface.
Therefore, when envisioning AI’s future, we must always consider two things together: one is the clear trajectory of technological advancement; the other is the physical and industrial foundation supporting that progress. AI models will continue to improve—becoming smarter through more complex memory architectures, advancing beyond mere recall into deeper reasoning, and delivering results in increasingly diverse formats.However, behind the scenes, increasing computational costs and hardware burdens will accumulate, eventually creating the next growth bottleneck and likely regulating the long-term pace of the market.
It’s uncertain when this will fully emerge in the market, but we must observe the aggressive price declines in the hardware market since Q1, leading to bleeding competition, as well as the anticipated hardware price increases in Q3. While lower prices create justification for expanded demand, once prices begin rising again, both enterprises and individuals are likely to adopt a more conservative stance, carefully reassessing their budgets—some companies are already tapping into their internal reserves. These shifts are not merely hardware issues; they may also impact the pace of AI advancement and valuations across the broader industry.
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While reading this article, I found myself reflecting on these scattered thoughts and wanted to organize them. I posed a single prompt to four AI models—AnumaAI’s ChatGPT 5.4, Claude Sonnet 4.6, Gemini 3.1 Pro, and Qwen 3.6 Plus—asking them for their perspectives on “AI model improvement and bottlenecks.” Of course, these models don’t provide “correct answers.” But they excel at rapidly offering multiple viewpoints and helping to clarify the branches of thought—particularly valuable in research, perspective-taking, and direction-finding tasks. I believe such a multi-model environment offers significant advantages in these contexts.
Ultimately, the future competition among AI platforms won’t end with “who has the smarter model.” What will matter far more is who delivers a better interface, who provides more consistent memory layers, and who can seamlessly integrate the strengths of multiple models into actual workflows. In this sense, Anuma is evolving beyond a mere aggregation of models into a personalized platform for AI-powered workflows.
As highlighted in the quote from Anuma Korea @AnumaAI_KR, if you’re following this trend closely, consider setting notifications and following along. AI is transitioning into an ecosystem competition where infrastructure, interfaces, memory, and execution environments collectively influence real-world applications—pay close attention as it moves into the next cycle. Thank you.
