NVIDIA is quietly constructing competitive moats along two strategic fronts that the market has yet to fully price in.

According to a research note released by HSBC on August 20, analyst Frank Lee believes that beyond the sustained run of earnings beats, the fresh catalysts driving a re-rating of NVIDIA's stock will come from two narrative threads that have so far been overlooked by investors.

First, the company's aggressive bet on open-source small language models (SLMs) promises to broaden its potential customer base from a handful of hyperscale cloud providers to millions of developers and sovereign states. Second, a series of multi-year procurement agreements have locked in supply chain capacity well in advance, an advantage that will become increasingly pronounced as rivals find it harder to secure critical manufacturing resources.

The market significance of these two narratives is that they both point to the same core proposition: NVIDIA's growth engine is evolving from a concentrated structure reliant on a few hyperscale clients toward a broader and more resilient customer ecosystem. Should this transition gain market recognition, it will be a key variable driving a re-rating of the company's valuation.

Open-Source Model Offensive: From Selling Shovels to Forging Them

The HSBC report notes that following the fading of the sovereign AI and neocloud boom, NVIDIA had struggled to establish a new narrative strong enough to trigger a significant share price re-rating, which is a key reason it has underperformed the Philadelphia Semiconductor Index year-to-date. The strategic push into open-source AI is now filling that gap.

NVIDIA is actively expanding its presence in the open-source model space, positioning itself as the world's largest contributor to open-source AI. According to company disclosures, its open-source model catalog has become the second most popular category by token generation volume. HSBC believes the strategic significance of this move lies in the fact that open-source SLMs are emerging as the preferred inference engine for AI agents and on-device applications.

The growing preference for SLMs is driven by three core factors:

Latency and throughput advantages — autonomous agents operate in high-frequency execution loops involving intent parsing, API calls, and outcome evaluation. Invoking large frontier language models (LLMs) at every micro-step would create severe latency bottlenecks, whereas SLMs offer significant advantages in both inference cost and throughput.

Task-specific intelligence — SLMs excel at constrained, deterministic tasks, and enterprises are embedding domain-specific SLMs into software platforms to tackle complex business problems.

Edge deployment capability — models with fewer than 10 billion parameters can be easily loaded into local GPU memory or edge devices, enabling localized operation.

NVIDIA's open-source product matrix is already comprehensive, covering multiple core scenarios:

The Nemotron series focuses on reasoning and language tasks. Cosmos targets "physical AI" in robotics and vision. GR00T N1 is positioned as the world's first open general-purpose foundation model for humanoid robots. Alpamayo is dedicated to autonomous driving scenarios. The NVIDIA Agent Toolkit and NeMo are used to build and customize enterprise-grade AI agents and generative AI applications, respectively.

HSBC points out that this free and highly optimized open-source ecosystem serves, in essence, as a strategic lever for NVIDIA to steer developers toward running their AI applications on its hardware first.

Supply Chain Positioning: Locking In Capacity to Build Competitive Barriers

The HSBC report highlights that AI compute demand remains in a state of persistent undersupply amid capacity constraints. NVIDIA has systematically secured key supply capacity across advanced packaging, memory, optical components, and energy infrastructure for 2026 through a series of multi-year agreements.

HSBC assesses that supply chain constraints across multiple segments could intensify further in 2027, at which point NVIDIA's advance procurement strategy will deliver competitive value far exceeding that of its peers and is likely to command a higher premium from the market.

According to The Information, in advanced packaging and memory, NVIDIA signed a $1.5 billion multi-year agreement with Amkor Technology in July 2026 to support the expansion of advanced semiconductor packaging and testing capacity in Arizona. In the same month, it reached a comprehensive cooperation agreement with the SK Group valued at up to $500 billion, covering joint development of next-generation AI memory (including HBM) with SK Hynix, as well as plans to build a 2-gigawatt Vera Rubin AI factory in South Korea.

In foundry capacity, NVIDIA has reserved 63% of TSMC's CoWoS-L advanced packaging capacity for 2026 and 52% for 2027. This forces GPU and ASIC competitors to seek alternatives from other suppliers, who face potential yield risks.

In optical interconnect, as AI network infrastructure transitions from copper cables to optical links, NVIDIA has signed multi-year strategic agreements with Lumentum and Coherent, committing $2 billion each to support R&D and US-based manufacturing capacity, while securing future access to advanced laser component capacity. It has also entered into a multi-year commercial and technology agreement with Corning to expand US-based manufacturing of advanced optical connectivity solutions.

In energy and land, NVIDIA is securing critical assets by directly taking equity stakes in infrastructure developers. According to Bloomberg, NVIDIA has invested in Cloverleaf Infrastructure, Lancium, and SB Energy, tying up power resources to ensure its chips will have "a place to run" in the future, while also embedding its full hardware and software stack into the early design phase of these facilities.

In one notable move, NVIDIA announced a partnership with SB Energy and OpenAI to secure land, power, and building capacity at the PORTS-Pike technology park in Ohio, with initial designs supporting 4.25 IT-GW of AI factory capacity. NVIDIA's cumulative payment obligations for this project cap at $105 billion, alongside a $1.5 billion investment in SB Energy.

Additionally, NVIDIA plans to invest $1 billion in NAVER to expand the "GAK Sejong" AI factory from 55 megawatts to 200 megawatts by 2028, with long-term plans to scale toward 1 gigawatt of sovereign AI infrastructure.

From a competitive landscape perspective, these investments essentially constitute a bundling strategy. Cloud giants and AI labs typically mix and match multiple suppliers when procuring chips, networking equipment, and custom cabling. By holding equity in infrastructure developers, NVIDIA gains significant leverage in ensuring that future facilities are designed around its complete technology stack.