Muse Reshapes the AI Industry Chain: Multi-Layer Tracks Reap Dividends, 3 Types of Companies Under Pressure

From the body of the fallen crayfish, Meta Muse has been born, and everyone must take this seriously. This is not the concept of another hit app; it is the landmark event marking AI’s formal shift from the model-training era into the agent-deployment era. Muse completely overturns the GPU-first compute logic, tears open the value gap between the “entry point” and “infrastructure” of internet platforms, and reconstructs a new five-layer AI industry chain. The curtain is now slowly rising on an industry-wide value reconstruction.

Muse: The First Consumer Agent That Actually Works Starts the Second Chapter of Compute

Muse is the first consumer AI Agent that can autonomously close the loop in the background: sending emails, booking travel, canceling subscriptions, filling out forms, comparing prices and placing orders. It keeps running the workflow even after you close the app. After launching on September 8, the numbers broke records: 2.8 million downloads in the U.S. and Canada in 12 days, more than 2× ChatGPT over the same period; over 500,000 users in the first week, and a single-day U.S. download peak of 264,000 to top the App Store. Actual usage is 10× internal testing, and Meta Chief AI Officer Alexandr Wang admitted it far exceeded expectations.

On the business model, Zuck has clearly chosen a transaction take-rate route: they only charge when the task gets done, which is completely different from subscription and advertising models.

What is most disruptive to the industry is the compute structure: GPUs in Meta data centers are only responsible for “thinking” (task decomposition, path planning), while CPUs are fully responsible for “doing” (opening browsers, logging into accounts, submitting operations). In user tests, Muse’s dedicated cloud computer has no discrete GPU; just 2 virtual CPUs + memory + storage can run the entire process.

Meta has 3.6 billion global daily active users, and now even a small-scale grayscale rollout has exploded compute demand. This is no longer a question of “do Agents need compute”; it is a question of “how many CPUs do Agents need.”

Compute Industry: CPU's Comeback — a 20x Supply Gap Ushers in a Supercycle

For the past three years, AI investing meant counting GPUs — and nothing else. Muse has pushed CPUs straight from supporting role onto center stage. The training era competes on GPUs, the Agent era competes on CPUs — the compute logic is starting to reverse.

1. The Ratio Shifts from 1:8 to 1:1, CPU's Status Jumps Exponentially

CPU/GPU ratios vary wildly by scenario:

• AI training: 1 CPU to 7–8 GPUs — CPU is purely auxiliary;

• Standard inference: 1 CPU to 1–3 GPUs — status rises;

• AI Agent: the ratio flattens all the way to 1:1, and in complex scenarios can reach 4:1.