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Welcome to Thoughts on the Market. I'm Sean Kim, head of Morgan Stanley's Europe and Asia Technology team. Today, a foundational shift in the development of AI and its broad market implications. It's Tuesday, May 5th at 3pm in London. Think about the last time you asked a chatbot to write a summary or a draft, or maybe answer a query. It was probably useful, but you are also still driving the interaction, asking, refining, copying, checking and moving the work forward. Now imagine a system that does not just respond, but acts it remembers what you asked last week, understands your preferences, works across digital tools, plans a workflow and adapts as circumstances change. That is a shift from gen AI to agentic AI, from AI that helps with thinking to AI that helps with doing. Genai is mostly passive. It takes a prompt and produces an answer. Agentic AI is active, less a copilot for one task, but an autopilot for multi step workflows. The distinction is key because computing requirements are changing. In Gen AI, large language models and GPUs handle much of the thinking. GPUs or graphics processing units process many calculations in parallel, making them central to modern AI models. In agentic AI, CPU becomes more important. CPUs or central processing units coordinate tasks and connect systems to the broader digital infrastructure. Agentic AI also depends on three the brain or or the large language model orchestration, where the CPU manages the doing and knowledge. Which is memory. Memory may be the most important layer. An agent that knows your preferences, documents, tone and task history becomes more useful over time. That creates a context flywheel. The more context it collects, the more personalized it becomes and the harder it is to leave. Typically in computing we think of memory as storage. Mainly we need to rethink this. Memory is also continuity. When an AI system can use past experiences, memory becomes a long term state, shared knowledge and behavioral grounding. And that matters because LLMs have fixed context windows. Once a conversation exceeds that window, all the contents falls off. For simple questions that may be fine, but for a coding agent working across a large code base over days or weeks, it is a major limitation. Serious work requires persistent memory, short term orientation and active retrieval, remembering prior decisions, understanding changed files and finding relevant codes without the user pointing to every dependency. For investors, the implication is clear. Agentic AI changes the bottlenecks. We see CPUs as a new bottleneck, with memory seeing the highest content increase. We estimate as much as 60% or $60 billion of incremental CPU total addressable market by 2030 within a total CPU market of more than $100 billion. We also estimate up to 70% of incremental DRAM bit shipment tied to this theme. That makes us more positive on the supply chain, including memory, Foundry, substrate, CPU and memory interface, and capacitors and CPU sockets. These areas benefit from content growth, pricing, power and capacity constraints into 2027 as AI moves from answering questions to taking actions. Investors should watch the infrastructure behind the shift because in the agentic era, the next big AI leap is maybe less about the prompt, but more about the processor. Thanks for listening. If you enjoy the show, please leave us a review wherever you listen and share thoughts on the market with a friend or a colleague today.
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