After Meta (META.US)'s “Muse Moment,” who will usher in the next “explosive rise”?

The Zhitong Finance App learned that less than two weeks after Meta Platforms' (META.US) Muse AI smart was launched, the stock price responded to the enthusiasm of the market with a single-day increase of more than 11%. Evercore ISI analyst Mark Mahaney called it “a very intuitive reflection of successful product innovation,” and bluntly stated that the company's approximately $200 billion investment in AI “didn't go to waste.” This extravaganza of market capitalization growth of around $190 billion also showed Deepwater Asset Management managing partner Gene Munster a replicable model — when consumers actually use a product and find it really easy to use, the capital market will reprice it in an almost violent way.

Munster said on social networking platform X: “We knew Muse was coming half a year ago. The reason this stock has skyrocketed is because the wider market has now used this product and finally understood how good it is.”

According to his deduction, the same situation will be created in Apple (AAPL.US), Tesla (TSLA.US), and SpaceX (SPCX.US) products. The catalysts are different, but the underlying logic is consistent: product experience replaces narrative expectations and becomes a trigger for revaluation.

Muse's “Usability Moment”

To understand Munster's logic, we first need to see exactly what Muse did right. Muse can send emails, book trips, complete shopping, fill out forms, and even continue to perform tasks after the app closes. The number of iOS downloads exceeded 902,000 times within six days of launch, which is higher than the previous Meta AI app's record of 773,000 during the same period. As of September 18, the number of daily active users had reached 448,000, and the total number of downloads in the first five days was about 730,000. As of Monday, the service had been downloaded more than 2.5 million times, and was once the most downloaded free iPhone app in the US.

But what's really interesting isn't the numbers themselves. Muse runs on Meta's Muse Spark 1.3 model — it's not the strongest model on the market. Its appeal comes from the execution level: custom agents built around Facebook Marketplace, Instagram, Gmail, Google Calendar, and OpenTable that can plan, execute, and track multi-step tasks across apps. In other words, Muse's victory was not a victory for the model, but a victory for the “shell.” When model capabilities exceed the speed that can be absorbed by the product side and user side, the competitive advantage is transferred to the level of organization, scheduling, and packaging the model output into reliable work.

This explains why Meta's market capitalization growth was accompanied by Arm (ARM.US) rising 17.16%, Intel (INTC.US) rising 12.14%, and AMD (AMD.US) rising nearly 10% — while Nvidia (NVDA.US) only rose 2.30%. When the agent's operating unit changes from a single inference call to a sandbox environment that runs continuously, the CPU's value allocation pattern is being rewritten.

Wells Fargo raised Meta's price target from $640 to $796 and said the company “now has a story to tell about AI.”

Apple's two chances, but the second was the real test

Munster's attention, however, has turned elsewhere.

Munster first set his sights on Apple, and he divided Apple's potential catalyst into two stages. Right in front of you is the iPhone Duo — Apple's first folding screen phone. The device, which is priced at $1999 and has a 7.6-inch display when unfolded, will be shipped on October 23. Counterpoint expects to sell about 6 million units during the year, and IDC expects Apple to account for 40% of the folding screen market by the end of 2027.

TrendForce (TrendForce) estimates that the model will ship about 5 million units in 2026, which will help Apple gain about 24.8% market share in the folding screen market, second only to 35.1% of Samsung (SSNLF.US). This means that with just one product, Apple will be in the second tier on the folding screen circuit. However, the folding screen is essentially an iteration of the hardware form. It can attract the need for switching, but it is difficult to independently support an AI narrative market similar to that triggered by Muse.

The real test falls on personalized Siri. Apple officially released Siri AI based on the custom Google (GOOGL.US) Gemini model at WWDC 2026. The assistant can call personal contextual information from SMS, emails, and photos, understand what is being displayed on the screen, and complete multi-step operations between multiple applications. In terms of technical architecture, Apple uses a three-tier privacy system: the end-side model handles low-latency privacy tasks, private cloud computing processes medium-complexity requests, and the most complex reasoning is completed by a customized 1.2 trillion parameter Gemini model running on Google Cloud Nvidia Blackwell GPUs.

The problem is timing. More than two years have passed since the original promise of this feature, which has been repeatedly delayed and even led to consumer lawsuits. Munster's framework requires users to not only feel that Siri is “finally usable,” but “really inseparable” — according to Muse's standards, this requires Siri to evolve from a passive-responding voice assistant to an intelligent body that actively completes tasks. Currently, there is not enough user data to determine whether the balance architecture established by Apple between privacy and ability is sufficient to support this transition in experience.

Tesla: First FSD, then Cybercab, then Optimus

Munster placed Tesla's catalysts in a clear order of priority: FSD first, Cybercab second, and Optimus third. The sequence itself conveys a judgment—the further you go, the greater the uncertainty.

FSD v15 is called a “step change” by Tesla, and about 40% of the seven-track parallel software architecture has already been deployed in the Austin Robotaxi fleet. The architecture's parameters are about ten times larger than the previous version. Tesla AI chief Ashok Elluswamy said in early September that the 24-hour Robotaxi service will arrive “around next month”, provided that the next technology module in the v15 plan is merged. Currently, the paid Robotaxi network covers the six cities of Austin, Dallas, Houston, Miami, Orlando, and Tampa, and operates from 6 a.m. to 10 p.m. By July, the unsupervised fleet had driven more than 380,000 miles, and Tesla claims to have maintained a “perfect safety record.” The company set a Robotaxi cost target of $0.30 per mile.

On the Cybercab side, limited-pay rides were launched in Austin on September 4, but the Robotaxi network still mainly relies on the Model Y rather than large-scale deployment of dedicated vehicles without steering wheels or pedals. Musk warned in January that initial production of Cybercab and Optimus would be “extremely slow” to climb. In July, it was reported that Tesla no longer plans to achieve “mass production” of the three latest Cybercab, Semi, and Megapack 3 products in 2026.

Optimus's situation is more complicated. The Gen 3 design was finalized by an executive review hosted by Musk at the end of June 2026, ending the iteration of the three internal versions of Alpha, Beta, and C, which took more than three years. Tesla has issued parts procurement guidelines to suppliers. The goal is to reach a production capacity of 1,000 units per week in September and sprint to 2,500 units per week by the end of the year. But while acknowledging that the Cybercab plant is “impressive,” Oppenheimer warns that Optimus's climbing capacity is “likely” to face further delays.

Tesla's stock price has fallen by about 17% during the year, which is the worst performer among the three targets Munster is optimistic about. Munster's arrangement essentially says: FSD is the only immediate catalyst, CyberCab needs FSD to prove itself first, and Optimus needs to run through the first two before having a basis for discussion.

SpaceX: the grandest and farthest bet

SpaceX's orbital data center program StarMind has the longest timeline and highest uncertainty among Munster's listed catalysts.

SpaceX has reached a partnership with Nvidia, and the first Starmind AI1 satellite will use Nvidia's Vera Rubin NVL72 rack-scale system. Musk said during the earnings call that SpaceX will “exclusively use Nvidia's AI architecture.” The solar array of the first satellite had an output power of up to 210 kW.

In terms of schedule, the target window for the first orbital calculation launch is the fourth quarter of 2027, and large-scale deployment is expected in 2028. SpaceX President Gwynne Shotwell revealed that Anthropic and Google have leased orbital capacity. In documents submitted to the FCC, SpaceX is seeking authorization to deploy up to 1 million StarMind satellites, each weighing 4,000 kilograms.

The primary challenge facing a constellation of this size is not technology, but launch capability. Related reports quoted analysts' calculations as pointing out that to maintain the constellation of 1 million satellites, SpaceX needs to launch more than nine starship rockets every day. As of September 2026, the Starship has only completed more than 10 test flights, and the FAA approved limit of 44 launches per year in Florida. Musk has stated that it will take four years for the Starship to achieve a frequency of “more than one launch per hour.”

The regulatory framework for rail data centers is also in a vacuum. Currently, there is no specific license category for “space data centers,” and the supervisory authority of large-scale commercial data center satellites during the launch process is unclear. More importantly, when data is processed in orbit, there is no consensus on which country's laws apply and how to define data sovereignty.

J.P. Morgan has outlined a potential path to 75 gigawatts of orbital computing power by 2031. However, if this figure is placed in the context of the global data center market, it will still require significant infrastructure investment and international regulatory coordination to be implemented.