In the wake of AI bubble concerns that arose five months ago, NVIDIA (NASDAQ: NVDA) has returned to the pinnacle of global markets. On Friday, the stock surged as much as 3%, hitting a record high of $237.88 and pushing its market capitalization above $5.7 trillion at one point—putting it within striking distance of $6 trillion.

Dan Bin, chairman of Dongfang Harbor, who had long held NVIDIA as the top position in his fund, was thrilled by the breakout. "Two years ago, I predicted NVIDIA could reach a $10 trillion market cap. For a while, despite better-than-expected earnings, the stock remained subdued. Today it finally hit a record high, though it's still some way from that $10 trillion target," he said. "The AI transformation is a once-in-a-century platform-level opportunity, and commercialization is accelerating. Companies continue to generate strong cash flow, investing in R&D to build technological moats while rewarding shareholders through large buybacks. The long-term growth logic hasn't changed, but realizing the market value of a great company takes time and continuous industry validation."

Two catalysts are driving NVIDIA's return to the top. First, U.S. September nonfarm payrolls came in below expectations, further reducing the probability of an October rate hike, which is favorable for risk assets. CICC believes the Fed needed a rate hike in September to protect its reputation and market trust, but there is no need for consecutive or large hikes, as fundamentals in traditional sectors don't support it—unless oil prices spiral out of control. Second, Morgan Stanley semiconductor analyst Joseph Moore's team reiterated NVIDIA as a top pick on October 2, giving it an overweight rating with a bullish view and a $300 base case target price—30% above the October 1 price of $230.86, corresponding to a $7.23 trillion market cap. This article will detail the key points from Morgan Stanley's aggressively bullish NVIDIA research report, which contains five incremental takeaways.

First, NVIDIA's customer base is breaking out of its traditional mold. The ACIE group (AI model companies, sovereign compute clusters, ODM/OEMs, and traditional enterprises) now contributes half of revenue, with growth significantly outpacing hyperscale cloud providers. Morgan Stanley notes that 90% of past market analysis focused only on orders from the top five cloud providers, completely underestimating the entirely new incremental demand from sovereign compute buildouts (using domestic land, power, and enclosures to create new businesses), as well as high-margin software service revenue from numerous revenue-sharing agreements.

Second, shifting AI bottlenecks now favor NVIDIA. Over the past year, the biggest AI bottleneck was the supply of GPUs and other semiconductors; now, the bottleneck is gradually shifting to data center land, power, facility construction, and financing capacity. The future of AI data centers isn't just about the number of chips, but how much compute can be generated per unit of power. Morgan Stanley's industry channel feedback indicates NVIDIA's token output per gigawatt is several times that of peers. So if gigawatts are the constraint, "gigawatt densification" (operating within a smaller power envelope) is likely to become the dominant strategy. According to Morgan Stanley, NVIDIA's Vera Rubin architecture achieves a 25x increase in token output per GW, while the Feynman architecture pushes revenue per GW from $40 billion to over $50 billion—turning competitors' "low chip procurement cost" into a disadvantage of "low output per unit of power."

Third, the bar for FY28 results is extremely low, with significant upside potential. Morgan Stanley calculates that NVIDIA faces almost no difficulty in hitting its FY28 data center revenue guidance: incremental contributions from just two customers—SpaceX and Amazon—account for over 75% of hyperscale segment growth, while all other customers need only modest growth to meet targets. After excluding new contributions from the standalone Vera CPU and Groq LPU, core GPU sales need only grow 7.3% year-over-year with a 57% increase in ASP to achieve the full-year target of 70% total revenue growth.

Fourth, a 72-73% gross margin is a solid safety cushion. There are two main reasons. First, price increases. If the next-generation Rubin platform raises prices, NVIDIA can pass on some cost increases to customers by raising product ASPs. Second, spec reductions. If HBM costs continue to rise, NVIDIA can optimize memory configurations to control costs without significantly affecting overall system performance. Morgan Stanley estimates that even with notable cost increases, as long as NVIDIA can hedge through product pricing and HBM configuration optimization, gross margins should remain at a high level—reflecting NVIDIA's very strong pricing power.

Fifth, a $500 billion ecosystem moat. NVIDIA has mobilized $500 billion in capital from outside its own system to invest in AI data centers, locking in components, power, and data center leases in advance before transferring them to downstream customers. It is also promoting mortgage valuation standards for AI factory assets, evolving from a hardware supplier into an organizer of AI compute infrastructure—completely transcending the traditional boundaries of semiconductor companies.

As for why it reiterates NVIDIA as a top pick, Morgan Stanley says NVIDIA is still in the very early stages of a new product cycle ramp; power and enclosure constraints happen to be NVIDIA's advantage; and recent agentic enthusiasm could accelerate Vera adoption. Market participants summarize it in four points: First, GPU supply remains tight. AI compute demand is still very strong, and the Blackwell and Rubin new product cycles are still in the ramp-up phase. Second, the power bottleneck actually strengthens NVIDIA's advantage. As data centers shift from GPU shortages to power shortages, the value of products that can deliver more compute per unit of power rises further. Third, agents may unlock new token demand. Agents not only increase CPU demand but may also drive larger inference compute volumes, further boosting GPU demand. Fourth, NVIDIA is transforming from a GPU supplier into an AI infrastructure platform. Beyond GPUs, Vera, Groq, NVLink, DPU, networking, and software ecosystems could all become new revenue growth drivers. Additionally, Morgan Stanley notes NVIDIA's stock is still relatively cheap at 15x FY28 EPS. If AI sentiment returns, there is room for valuation expansion—but even if it doesn't, continuously upward-revised earnings alone can drive the stock higher.