- The next wonder-app should be an AI-model that seamlessly uses browsers, desktops, databases, and communication tools to execute complex, multi-week human jobs
- “Agent to Agent Communication” could become the next go-to app bypassing human middleware
- The industry pivot from model training toward real-time inferencing is creating structural demand for HBM and advanced DRAM as memory intensity rises
- We remain constructive on the technology theme and segments that benefit from the proliferation of AI usage
As AI expands from coding into broader business operations like customer support and enterprise search, “Agent to Agent Communication” is emerging as a new growth driver enabled by Model Context Protocol (MCP). Furthermore, a massive industry pivot from model training to real-time inferencing is creating massive, long-term demand for High Bandwidth Memory and advanced DRAM hardware. We prefer full-stack Large Language Model (LLM) providers and Advanced Memory players.
“Agent to Agent Communication” could become the next go-to app. Coding is the perfect first wonder-app for AI because code is a closed, logical loop: the computer gives instant error feedback, allowing the AI to self-correct. Consequently, engineering roles are shifting from writing code to acting as AI architects and reviewers. The next wonder-app will likely be an AI model that seamlessly uses browsers, desktops, and available databases, and communication tools to execute complex, multi-week human jobs. For instance, tools like OpenAI's “Operator” and Anthropic's “Claude Computer Use” can view a desktop screen, move the mouse, click buttons, and type text. These tools can interact with multiple agents on the users’ behalf based on Model Context Protocol (MCP), an open-source technology. Just as USB ports standardised hardware connections, MCP allows any AI model to plug and play with local files, databases, and APIs. As businesses deploy their own specialised AI agents, these agents will begin talking to each other, completely bypassing human middleware. For example, onboarding a new hire requires a massive headache of communication between the HR, IT, and Finance departments. In an agentic office, the human HR manager just clicks "Approve Hire" and the AI agents will take over. A specialised HR agent can automatically tell an IT provisioning agent to order a laptop, create an email account, and assign software license - the moment a contract is signed. This not only saves onboarding time but also reduces significant operational costs.
In line and above expected results in the latest quarter. Alphabet reported strong 1Q26 results, with normalised EBIT reaching USD39.7bn (+30% y/y), exceeding consensus by 9%, while revenue grew 22% y/y, 3% above expectations. Google Cloud revenue rose 63% y/y to USD20.0bn, with Cloud EBIT surging 203% and margins expanding to 33% on strong AI demand. The company also increased FY26 capex guidance to USD180–190bn from USD91.4bn in 2025 to support AI compute needs. Micron delivered a 33% EPS beat driven by stronger memory pricing and product mix. While Alibaba’s AI investments have temporarily dragged margins despite cloud growth accelerating to 40% y/y, it has guided positive outlook for future profitability. Street projects Alibaba to incur RMB150bn capex in 2026 vs RMB120bn in 2025 mainly for AI computing needs.
The 2026 Architectural pivot: the rise of mass inferencing and full-stack dominance. The industry is undergoing a major shift from foundation model training toward large-scale, real-time inferencing. As advanced enterprise AI models increasingly move into production, demand for memory-intensive infrastructure is rising sharply. The bottleneck in the AI infrastructure buildout is shifting from GPUs to High Bandwidth Memory (HBM). This creates long-term growth drivers for next-generation HBM and dense DRAM solutions needed to support higher data processing requirements.
At the same time, the market is consolidating around full-stack LLM providers that integrate specialised chips, cloud infrastructure, and proprietary models, enabling them to capture a larger share of industry value and generate stronger near-term revenue growth.
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