Thoughts on the Market
AI’s Shift From Thinking to Taking Action
Why It Matters
Understanding this shift is vital because the next wave of AI value will be driven by hardware that supports autonomous, memory‑rich agents, reshaping investment opportunities across the semiconductor and memory supply chain. As AI moves from answering questions to executing tasks, the demand for CPUs and persistent memory will surge, making these sectors key growth areas for the next decade.
Key Takeaways
- •Agentic AI shifts from answering to autonomous action.
- •CPUs become primary bottleneck, overtaking GPUs in AI workloads.
- •Persistent memory enables long‑term context for AI agents.
- •AI infrastructure could add $60B CPU, $70B DRAM by 2030.
- •Investors should target memory, CPU, and related supply chain.
Pulse Analysis
During the latest episode of Thoughts on the Market, Sean Kim explained that artificial intelligence is moving beyond the traditional generative‑AI model, which merely responds to prompts, toward what he calls agentic AI. Unlike a chatbot that waits for user direction, an agentic system can remember past interactions, schedule tasks across multiple tools, and adapt its workflow as conditions change. This transition from “thinking” to “doing” reshapes how enterprises automate processes, turning AI from a supportive assistant into an autonomous executor that can manage end‑to‑end operations.
The architectural shift has immediate hardware consequences. In generative‑AI workloads GPUs dominate because they excel at parallel matrix calculations, but agentic AI relies heavily on CPUs to orchestrate tasks, interface with digital infrastructure, and retrieve persistent memory. Memory itself becomes a strategic layer, providing long‑term state that overcomes the limited context windows of large language models. As agents accumulate preferences, documents, and task histories, a “context flywheel” emerges, delivering ever‑more personalized output while demanding larger DRAM capacities and faster CPU‑memory interfaces to sustain real‑time action.
From an investment perspective, the move to agentic AI creates a new growth frontier. Morgan Stanley projects up to $60 billion of incremental CPU revenue and roughly $70 billion of DRAM shipments by 2030, driven by the need for higher‑performance processors and expanded memory pools. Consequently, supply‑chain segments such as silicon foundries, substrate manufacturers, CPU sockets, and capacitor producers stand to benefit from sustained pricing power and capacity constraints through 2027. Investors should therefore monitor hardware roadmaps, memory‑technology advances, and companies positioned to supply the orchestration layer that powers autonomous AI agents.
Episode Description
Our Head of Europe and Asia Technology Research Shawn Kim discusses AI’s move from passive chatbots to active agents—and how this influences tech supply chains.
Read more insights from Morgan Stanley.
----- Transcript -----
Welcome to Thoughts on the Market. I’m Shawn 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 were 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 the shift from GenAI 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 GenAI, 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 stacks: the brain, 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, older content falls off. For simple questions, that may be fine. But for a coding agent working across a large codebase 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 the new bottleneck, with memory seeing the highest content increase. We estimate as much as 60 percent, 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 percent of incremental DRAM bit shipment tied to this theme.
That makes us more positive on supply chains including memory, foundry, substrates, 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 may be 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 colleague today.
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