#摩根大通将Meta目标价上调至820 JPMorgan: Meta’s models and agents are in place, upgraded to a moderately positive view!
Met
The relevant market price has rebounded 20% from its recent low, but is still down 1% year to date, while the S&P 500 has risen 12% over the same period. The core judgment of this JPM report is that Meta’s in-house model team has largely delivered on its goal of reaching the frontier within a year. From July’s Muse Spark 1.1 to the recent 1.3, it can now rank alongside the Claude and GPT series on benchmarks covering agents, coding, instruction following, and long-context capabilities. This model capability is the prerequisite for Meta to open up revenue streams from agents, enterprise tools, and developer interfaces beyond advertising.
First, the models themselves. Muse Spark 1.3 is the latest version, and the report believes its competitiveness comes from a rapid iteration cycle, attractive pricing, and continuously expanding computing power. The next-generation Watermelon model has undergone more advanced pretraining and is expected to launch soon, while models following Watermelon are already being trained at scale on the Prometheus GW cluster in Ohio. Whether model quality can continue to keep pace will determine the ceiling for all subsequent monetization paths. 1. Muse agent reaches third place in the US App Store free rankings two days after launchMuse is Meta’s consumer-focused personal agent and has launched for US users on iOS, Android, and the web. It uses its own virtual computer to browse websites and operate interfaces, helping users complete tasks such as online shopping, ordering food, filling out forms, and communicating via email and text messages. Its training covers platforms including Instagram, WhatsApp, Spotify, DoorDash, Etsy, Reddit, Yelp, and Outlook/Gmail. Early data provides two reference points: Muse usage reached 10 times the size of the test queue, and it rose to third place in the US Apple App Store’s free app download rankings on the second day after launch. Muse is currently free and provides 100 million tokens per week. The paid tiers offer 500 million tokens per month for $20 per month and 3 billion tokens per month for $100 per month. The report believes Meta’s current focus is not monetization, but building awareness, adoption, and usage habits; it is more likely to adopt a transaction-fee or commission model in the future.
Reaching third place in the free rankings on the second day shows that Meta’s distribution capabilities can indeed quickly put a product in front of users, but download rankings and retention are two different things. I am more focused on whether usage 10 times the size of the test queue can persist after the free quota is exhausted, as this will determine whether the commission model has a real transaction base.
2. Enterprise agents charge by token, with over 1 million businesses already using them weekly
Meta Business Agent is a plug-and-play tool used via subscription. It can answer questions, mention products, schedule appointments, and qualify leads. As of Q2 2026, more than 1 million businesses were using the agent weekly on WhatsApp and Messenger. Business Agent Platform is an API-based enterprise layer that can connect to systems such as Shopify, Zendesk, and Shopee, allowing agents to perform actions on behalf of businesses. The billing model has already been implemented: starting August 1, API and enterprise users will be charged by token, at $2 per 1 million tokens. A single message consumes 20,000 to 25,000 tokens, equivalent to approximately 4 to 5 cents per message. Management also mentioned that enterprise-agent monetization could eventually evolve into an outcome-oriented bidding system similar to the advertising business. The potential of this segment lies in Meta’s existing relationships with millions of advertisers and small and medium-sized businesses, allowing agent adoption to reuse this distribution channel.
3. Model interfaces and coding agents compete for developer share with low pricing$META

#Gate24HFuturesOpenInterestTops$11.479B For this SNDK move, don’t just look at the K-line—watch U.S. stocks.
It’s not a shitcoin, but a stock perpetual tracking SanDisk’s share price. Last Friday, SanDisk surged 11.9% in a single day to lead the S&P 500. Two more catalysts are ahead: inclusion in the S&P 100 on 9/21, prompting passive buying by index funds, and continued increases in NAND contract prices in Q3, marking a storage supercycle.
The perpetual is currently at 1790, with only ~3% premium to SanDisk’s U.S. closing price—not expensive. Technically, the pullback to 1760 on declining volume held, keeping the structure healthy.
👉 Strategy: Scale into longs above 1760; add to the position after holding above 1801 with rising volume. Targets: 1822 → 1850. Cut losses and exit if it breaks below 1760.
$SNDK #GateRWA永续合约持仓量全球第一

#AMD$2TAI2030
AMD's Two Trillion Dollar Bet: The Quiet Architecture of the AI Economy
There is a particular kind of ambition that reveals itself not in a single announcement, but in the steady accumulation of agreements that, taken together, describe a different future. Over the past twelve months, Advanced Micro Devices has assembled exactly that kind of portfolio. The company has secured multi-gigawatt commitments from three of the most consequential names in artificial intelligence: OpenAI, Meta, and Anthropic. And at a conference in New York earlier this month, its chief financial officer, Jean Hu, placed a number on what that portfolio might ultimately be worth: a total addressable market of between two and three trillion dollars by 2030.
That figure deserves to be read carefully. It is not a revenue forecast. It is an estimate of the size of the opportunity AMD believes it can address. But the distinction matters less than the trajectory it describes. In July, AMD estimated its addressable market would reach roughly two trillion dollars by the end of the decade. Two months later, it raised the upper bound to three trillion. When a company revises its view of its own opportunity upward by fifty percent in a single quarter, it is telling you something about the pace at which the ground beneath it is shifting.
The numbers behind the business are already moving quickly. In the second quarter of 2026, AMD reported total revenue of 11.5 billion dollars. Data center revenue alone reached 6.72 billion dollars, more than double the 3.2 billion dollars generated in the same period a year earlier, and up from 5.8 billion dollars in the prior quarter. That segment now accounts for fifty-eight percent of total revenue, and it is growing at a pace that has made it the largest and fastest-moving part of the company. The demand is coming from two sources: EPYC processors for server CPUs and Instinct accelerators for AI training and inference. Both are benefiting from the same underlying trend, which is the relentless expansion of computing infrastructure required to train and run increasingly capable models.
The customer agreements tell the story more vividly than the revenue figures alone. OpenAI has signed a six-gigawatt commitment, with the first gigawatt of MI450 GPUs scheduled for deployment in the second half of 2026. Meta has signed a comparable six-gigawatt agreement, covering multiple generations of Instinct accelerators. Anthropic has committed to deploying up to two gigawatts of MI450 GPUs through AMD's Helios rack-scale systems, and AMD is investing up to five billion dollars into the company as part of the arrangement. Taken together, these agreements represent twelve gigawatts of committed GPU capacity, a figure that would have been difficult to imagine for AMD's accelerator business even two years ago.
The strategic significance of these deals extends beyond the revenue they represent. For years, the AI accelerator market has been effectively a single-vendor market, with Nvidia capturing the overwhelming majority of spending. The emergence of a credible second source is consequential for every company that depends on AI infrastructure, because it introduces competition into a supply chain that has been characterized by allocation constraints and pricing power concentrated in one firm. AMD's ability to win these commitments suggests that the largest AI developers are willing to invest in a second platform, not necessarily to replace the incumbent, but to ensure that they are not entirely dependent on it. The motivation is partly commercial and partly strategic, and both are rational.
The Helios platform is central to this effort. Announced at AMD's Advancing AI conference in July, Helios is a rack-scale system designed to compete directly with Nvidia's rack-scale offerings. It integrates AMD's Instinct GPUs, EPYC CPUs, and networking components into a single architecture, and it is scheduled to begin shipping in the second half of 2026, with volumes increasing into 2027. The importance of a rack-scale approach is that it allows customers to deploy AI infrastructure more efficiently, with fewer integration challenges and better performance per unit of power and space. For a company like Anthropic, which is building out server infrastructure at a rapid pace, the appeal of a pre-integrated system is straightforward.
The financial implications of this buildout are beginning to show in AMD's guidance. The company has said it expects data center revenue to reach approximately seventy billion dollars in 2027, a figure that would represent a substantial step up from current levels. It has also raised its forecast for the server CPU market to 220 billion dollars by 2030, up from a prior estimate of about sixty billion. These are not modest revisions. They reflect a view that the demand for computing infrastructure is not a cyclical phenomenon but a structural shift, driven by the recognition across every major industry that AI capabilities will be foundational to competitive advantage.
Yet it would be incomplete to describe this story without acknowledging the risks. The commitments from OpenAI, Meta, and Anthropic are large, but they are also concentrated. If any of these customers were to slow their spending, whether because of funding constraints, strategic shifts, or a broader recalibration of AI investment, the impact on AMD's outlook would be significant. The company is also competing against an incumbent that has spent years building not just hardware but an entire software ecosystem around its platform. AMD's software stack, ROCm, has improved considerably, but it remains a work in progress relative to the maturity of the alternative. Finally, the capital intensity of this buildout is substantial. AMD is investing billions into Anthropic and into its own manufacturing and research capacity, and those investments will weigh on near-term profitability even as they lay the groundwork for future growth.
For those who follow digital asset markets, the AMD story offers a useful lens. The AI infrastructure cycle is one of the most powerful forces in the global economy right now, and it is shaping capital flows, energy demand, and corporate strategy in ways that extend far beyond the technology sector. The same data centers that train large language models are being designed to accommodate tokenized financial infrastructure, and the same institutional investors funding AI buildouts are the ones allocating capital to digital assets. The two worlds are becoming harder to separate, and AMD sits at the intersection of them.
What should a careful observer watch in the coming quarters? First, the delivery timeline for Helios. The first deployments are expected in the second half of 2026, and execution on that schedule will determine whether the commitments convert into revenue on the expected timeline. Second, the trajectory of data center revenue. The seventy billion dollar target for 2027 is ambitious, and quarterly progress toward it will be the clearest signal of whether the demand is as durable as the agreements suggest. Third, the broader AI investment environment. The same macroeconomic pressures that weigh on every risk asset, including the Federal Reserve's rate path and the cost of capital, will influence how aggressively AMD's customers deploy their committed capacity.
The deeper truth is that AMD is no longer simply a semiconductor company competing for share in a mature market. It is a participant in the construction of an entirely new layer of economic infrastructure, one that will determine how intelligence is produced, distributed, and consumed for decades to come. The two trillion dollar figure is a measure of how large that infrastructure might become. Whether AMD captures a meaningful share of it will depend on execution, competition, and the willingness of its customers to follow through on the commitments they have made. The rest of us can only watch, calculate, and prepare.
$AMD ‌
$META ‌#ShareWeekly #Gate #STOCKS

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