Last night, Meta’s stock surged by more than 11% in a single day. Intel and AMD rose by about 12% and 10%, respectively, mainly because Meta’s consumer AI agent product, Muse, has continued to heat up. This is worth discussing because it may be rewriting the underlying logic of “AI compute.”
1. What exactly did Muse change?
Most consumer-level AI on the market is still, in essence, a Q&A tool: you input some text, the model reasons and returns an answer, and the task is over. Muse pulls AI out of the chat window and into a real work workflow. You set a goal, it breaks the job into steps—signs in, visits websites, calls external services, reads page status—and then decides what to do next. When it encounters actions like payments, sending emails, or rescheduling, it proactively asks for your authorization, completes the task, and then checks whether the job is finished.
The closed loop is: understand the goal → formulate a plan → call tools → observe results → adjust the plan → continue execution.
In traditional AI, the “work unit” is a single “conversation.” In an agent, the “work unit” is a complete “task.” Meta also provides a cloud-based persistent execution environment: even if you close the app, it keeps running in the background. This means competition is no longer only about the base model—it also involves distribution entry points, user data, and the business ecosystem. Meta controls vast user entry channels. Once Muse has accumulated emails, calendars, spending habits, and task history, switching costs become enormously high—not just moving chat history, but moving an entire way of working.
2. Why did this pull focus onto CPUs?
For the past few years, GPUs have been the main battlefield for AI infrastructure. But once agents enter real workflows, the compute structure changes: GPUs handle the “thinking” part—understanding the goal, generating plans, and interpreting page content. The “doing” part—running systems, starting virtual machines, calling APIs, managing permissions, and tracking task state—gets pushed onto CPUs and traditional servers.
A chat model’s pipeline is: input → infer → output → release resources. An agent’s pipeline is: infer → CPU execution → wait for external responses → re-read state → infer again → execute again. A single long task may loop dozens or even hundreds of times. Browsers, virtual machines, memory, and task state need to stay online continuously. AI compute demand expands from “generating tokens” to “the full system resources required to complete a task.”
Once Muse is opened to billions of users, even if only a small portion runs agents simultaneously, it will generate massive concurrency: virtual machines, browser processes, and API calls. Consumer agents need to access email, payments, and account permissions. They can’t cram everyone’s tasks into the same environment. The more emphasis on secure isolation, the more independent resources must be allocated to each user. CPU demand in the agent era comes from three lines: increased concurrency, longer task durations, and tighter security isolation. This is also why the market is once again focusing on multi-core server CPUs. Some even predict CPU core demand could double, and the CPU-to-GPU ratio could approach 1:1.
One-sentence summary of the observation framework: when inference costs fall, the AI bottleneck will shift from “can the model think?” to “can the backend execute with sufficiently low-cost concurrency?”
3. How does compute demand translate into profits?
Demand doesn’t automatically equal profit—the key is Muse’s unit economics. With free and paid tiers at $20 and $100 subscriptions, it can also monetize later through enterprise agents, developer APIs, and transaction commissions. Muse can identify real purchase intent, so the theoretical value per user may be higher than traditional ad-click revenue. Some organizations estimate that future revenue could exceed $10 billion (one hundred-plus billion USD is implied).
However, agent cost structures are different: you must run virtual machines for users, occupy memory, burn network bandwidth, and call models. The harder you use it, the higher the backend costs. The two subscription tiers are essentially also about resource control—limiting task volume, limiting the number of calls, and limiting background runtime. Compared with fixed subscriptions, transaction commissions align more closely with the cost structure of agents.
But a tradable model can also spark platform conflicts. Amazon has already cut off Muse’s ability to shop inside its app. Once an agent searches and places orders on the user’s behalf, users skip recommendation pages and ads. Third-party platforms will likely go down one of three routes: charge for open interfaces, require transaction revenue sharing, or block unauthorized access. Any path will compress Muse’s service boundaries and profit margins.
4. How to understand this entire transmission chain
For Meta: Muse is the litmus test of whether AI capital expenditures can be converted into consumer revenue. Downloads prove distribution; completion rate determines value; retention and paid conversion determine revenue; unit task cost determines profit.
For Intel: GPUs handle thinking, CPUs handle execution. If consumer agents roll out at scale, server CPU may evolve from a supporting component into a critical factor determining concurrency scale and running costs.
The full transmission chain is: consumer agents get the user → tasks shift from Q&A to continuous execution → virtual machines, browsers, and tool calls surge → demand for CPU, memory, and networking rises → infrastructure costs increase → subscription and transaction revenue validate unit economics → chip demand ultimately turns into supplier profits.
In short: GPUs determine whether an agent can think things through clearly; CPUs and system infrastructure determine whether it can get enough people’s jobs done at the same time. And the final capital return depends on how much money is earned per completed task—whether it can cover the costs of the entire execution chain.
1. What exactly did Muse change?
Most consumer-level AI on the market is still, in essence, a Q&A tool: you input some text, the model reasons and returns an answer, and the task is over. Muse pulls AI out of the chat window and into a real work workflow. You set a goal, it breaks the job into steps—signs in, visits websites, calls external services, reads page status—and then decides what to do next. When it encounters actions like payments, sending emails, or rescheduling, it proactively asks for your authorization, completes the task, and then checks whether the job is finished.
The closed loop is: understand the goal → formulate a plan → call tools → observe results → adjust the plan → continue execution.
In traditional AI, the “work unit” is a single “conversation.” In an agent, the “work unit” is a complete “task.” Meta also provides a cloud-based persistent execution environment: even if you close the app, it keeps running in the background. This means competition is no longer only about the base model—it also involves distribution entry points, user data, and the business ecosystem. Meta controls vast user entry channels. Once Muse has accumulated emails, calendars, spending habits, and task history, switching costs become enormously high—not just moving chat history, but moving an entire way of working.
2. Why did this pull focus onto CPUs?
For the past few years, GPUs have been the main battlefield for AI infrastructure. But once agents enter real workflows, the compute structure changes: GPUs handle the “thinking” part—understanding the goal, generating plans, and interpreting page content. The “doing” part—running systems, starting virtual machines, calling APIs, managing permissions, and tracking task state—gets pushed onto CPUs and traditional servers.
A chat model’s pipeline is: input → infer → output → release resources. An agent’s pipeline is: infer → CPU execution → wait for external responses → re-read state → infer again → execute again. A single long task may loop dozens or even hundreds of times. Browsers, virtual machines, memory, and task state need to stay online continuously. AI compute demand expands from “generating tokens” to “the full system resources required to complete a task.”
Once Muse is opened to billions of users, even if only a small portion runs agents simultaneously, it will generate massive concurrency: virtual machines, browser processes, and API calls. Consumer agents need to access email, payments, and account permissions. They can’t cram everyone’s tasks into the same environment. The more emphasis on secure isolation, the more independent resources must be allocated to each user. CPU demand in the agent era comes from three lines: increased concurrency, longer task durations, and tighter security isolation. This is also why the market is once again focusing on multi-core server CPUs. Some even predict CPU core demand could double, and the CPU-to-GPU ratio could approach 1:1.
One-sentence summary of the observation framework: when inference costs fall, the AI bottleneck will shift from “can the model think?” to “can the backend execute with sufficiently low-cost concurrency?”
3. How does compute demand translate into profits?
Demand doesn’t automatically equal profit—the key is Muse’s unit economics. With free and paid tiers at $20 and $100 subscriptions, it can also monetize later through enterprise agents, developer APIs, and transaction commissions. Muse can identify real purchase intent, so the theoretical value per user may be higher than traditional ad-click revenue. Some organizations estimate that future revenue could exceed $10 billion (one hundred-plus billion USD is implied).
However, agent cost structures are different: you must run virtual machines for users, occupy memory, burn network bandwidth, and call models. The harder you use it, the higher the backend costs. The two subscription tiers are essentially also about resource control—limiting task volume, limiting the number of calls, and limiting background runtime. Compared with fixed subscriptions, transaction commissions align more closely with the cost structure of agents.
But a tradable model can also spark platform conflicts. Amazon has already cut off Muse’s ability to shop inside its app. Once an agent searches and places orders on the user’s behalf, users skip recommendation pages and ads. Third-party platforms will likely go down one of three routes: charge for open interfaces, require transaction revenue sharing, or block unauthorized access. Any path will compress Muse’s service boundaries and profit margins.
4. How to understand this entire transmission chain
For Meta: Muse is the litmus test of whether AI capital expenditures can be converted into consumer revenue. Downloads prove distribution; completion rate determines value; retention and paid conversion determine revenue; unit task cost determines profit.
For Intel: GPUs handle thinking, CPUs handle execution. If consumer agents roll out at scale, server CPU may evolve from a supporting component into a critical factor determining concurrency scale and running costs.
The full transmission chain is: consumer agents get the user → tasks shift from Q&A to continuous execution → virtual machines, browsers, and tool calls surge → demand for CPU, memory, and networking rises → infrastructure costs increase → subscription and transaction revenue validate unit economics → chip demand ultimately turns into supplier profits.
In short: GPUs determine whether an agent can think things through clearly; CPUs and system infrastructure determine whether it can get enough people’s jobs done at the same time. And the final capital return depends on how much money is earned per completed task—whether it can cover the costs of the entire execution chain.