Compare moats
Up to three covered companies, band by band. Every call is a curated editorial judgment, never a disclosed figure — and every band carries its cited basis.
| Hon Hai Precision (Foxconn) | Snowflake | NVIDIA | |
|---|---|---|---|
| Moat rating | narrow The advantage the company documents is real but sits almost entirely below the gross line: the 2Q26 release cites EMS market share that 'exceeds 40%', over 240 campuses across 24 countries and CNC/SMT automation 'close to 100%', while the same quarter's gross margin was 6.12% — 21 bps below 2Q25's 6.33% per the results deck — and operating margin was 3.75%. | narrow Narrow, not wide, because the FY2026 10-K documents a strong installed base and a competitive position the company itself says is under erosion. On the asset side: revenue of $4.7 billion (29% growth in each of the last three fiscal years), 13,328 total customers up from 10,996, 790 of the Forbes Global 2000 contributing about 43% of revenue, 733 customers above $1 million in trailing-12-month product revenue up from 576, a 125% net revenue retention rate, and more than 1,050 issued U.S. patents. Against that, Item 1A states plainly that adopting open data formats like Apache Iceberg means 'there is less customer “lock in” when our products are used in external environments' and that 'our support of open data formats may also reduce switching costs between us and our competitors'; that AWS, Azure and GCP 'generally compete in all of our markets' while also supplying the infrastructure a 'substantial majority of our business is run on'; and that the company remains loss-making at $1.3 billion of net loss for the year. | wide Wide: the CUDA software platform and its developer installed base create high switching costs across the AI-training stack, defended by $76.7B cumulative R&D and reinforced by ~71% FY2026 gross margins and Data Center revenue (~90% of total) up 68% YoY — durable, hard-to-replicate advantages per the FY2026 10-K. |
| Moat type | cost scale Rotating CEO Michael Chiang attributes the sustained operational performance to 'its global footprint and economies of scale', more than 240 sites in 24 countries, 'vertically integrated and highly automated operations' and 'a high in-house production ratio of key components'; the results deck's closing slide is titled 'Vertical integration of AI supply chain L1 to L12 optimizes cost structure'. | switching costs The filing makes its own affirmative claim of network effects — 'Our business benefits from powerful network effects. ... The more customers adopt our platform, the more data can be exchanged with other Snowflake customers, partners, data providers, and data consumers' — but the load-bearing, quantified evidence in the document points to switching costs. The platform is sold as the way to 'consolidate data into a single source of truth,' and the disclosed economics of that consolidation are a 125% net revenue retention rate and 733 customers above $1 million in trailing product revenue. Item 1A confirms the mechanism by naming what is at risk: open formats produce 'less customer “lock in”' and 'may also reduce switching costs.' The filing frames lock-in, not network density, as the thing erosion would take away. | intangibles ip The durable edge rests on proprietary IP and software: the full-stack CUDA development platform running on all NVIDIA GPUs plus hundreds of proprietary domain libraries/SDKs/APIs, and $76.7B cumulative R&D yielding "inventions that are essential to modern computing" (NVIDIA invented the GPU in 1999). This is reinforced by developer-ecosystem network effects and CUDA switching costs, per the FY2026 10-K Business section. |
| Leadership | clear leader Company-stated: the release describes Hon Hai Technology Group as 'the world's largest electronics manufacturer and leading technology solutions provider, ranking 23rd in Fortune Global 500', with EMS market share that 'exceeds 40%' across its four product segments. No independent ranking is cited in the source. | co leader The 10-K contains no ranking, market-share figure, or claim of leadership, and it names no non-hyperscaler competitor by name. The band rests on disclosed scale — $4.7 billion of revenue, 13,328 customers, 9,060 employees across 36 countries — set against the filing's own statement that 'many of our competitors have substantially greater brand recognition, customer relationships, and financial, technical, and other resources than we do.' Co-leader among independent cloud data platforms; not a leader over AWS, Azure and GCP, which the filing says compete in all of its markets. | clear leader FY2026 Data Center revenue was $193.7B (~90% of the $215.9B total), up 68% YoY on the Blackwell ramp, and NVIDIA describes itself as "a data center scale AI infrastructure company reshaping all industries." The 10-K frames named rivals as parties who "provide or intend to provide" GPUs/accelerators — incumbent-leader positioning. |
| Pricing power | weak Gross margin was 6.12% in 2Q26 and 6.15% in 1H26, and the results deck shows the quarter 21 bps below 2Q25's 6.33% even as revenue grew 41% year-on-year; the group frames profitability against its own 3% operating-margin target, met at 3.67% in 1H26 — margins of that order leave little headroom to price above cost. | moderate The consumption model plus 125% net revenue retention shows real expansion pricing, and the filing argues it competes on 'pricing transparency and optimized price-performance.' But Item 1A limits how far that goes: competition 'may negatively impact our ability to acquire new customers ... put downward pressure on our prices and gross margins'; the company 'may not be able to ... offer as many discounts or free services as our competitors'; results depend on 'changes in our pricing model, including in response to significant price discounts by our competitors' and on 'customer optimization efforts that result in reduced consumption.' On the cost side, 'our costs and gross margins are significantly influenced by the prices we are able to negotiate with these public cloud providers, which in certain cases are also our competitors.' | strong FY2026 gross margin was 71.1% (75.0% in FY2025); per the 10-K MD&A the ~3.9pt decline reflects the Hopper HGX→Blackwell full-system mix shift and a one-time $4.5B H20 excess-inventory/purchase-obligation charge, not competitive price erosion. A low-70s% hardware gross margin evidences strong pricing power. |
| Summary | Foxconn's own 2Q26 disclosure rests the business on scale and integration: over 240 campuses in 24 countries, EMS market share above 40%, CNC and SMT automation close to 100%, and an L1-to-L12 in-house chain for AI racks — high-speed cable, power busbars, sockets and connectors, enclosures, chassis, heatsinks, PCB/PCBA, compute and switch trays, manifolds, racks — which management says lets the group 'maintain sound operating efficiency and yields in the face of high unit price and highly complex AI products'. Cloud and networking products were 51% of 2Q26 revenue, the company says it 'continues to expand its market share in AI server racks', and its stated AI advantage is 'providing complete, end-to-end system solutions, rather than just single products'. The counterweight is in the same release: 6.12% gross margin and a 3.75% operating margin on NT$2.53 trillion of quarterly revenue. | Snowflake's advantage in its FY2026 10-K rests on being the consolidation point for enterprise data: a multi-cluster shared-data architecture with proprietary columnar storage and automatic micro-partitioning, delivered across three major public clouds and 53 interconnected regional deployments, that customers adopt as a single governed source of truth and then expand on — 125% net revenue retention, 790 of the Forbes Global 2000 as customers. The filing layers a collaboration claim on top, with sharing 'generally without copying or moving the underlying data' and a Marketplace of 'hundreds of live, ready-to-query third-party data sets and data products.' The same document is unusually candid about the counter-pressure: Iceberg and open formats reduce lock-in by the company's own account, the three hyperscalers compete across every market while setting the cloud costs that 'significantly influence' gross margins, and frontier AI model providers 'may seek to vertically integrate ... by expanding into the data storage and management layers.' | NVIDIA pairs market-leading accelerated-computing hardware (the Blackwell data-center platform) with a proprietary full-stack software moat — CUDA plus hundreds of domain libraries — funded by $76.7B of cumulative R&D, and its "large and growing number of developers and installed base... strengthens our ecosystem and increases the value of our platform for our customers" (FY2026 10-K). Competition is intensifying from AMD, Intel and Huawei, and from hyperscalers (Alphabet, Amazon, Microsoft) designing internal AI silicon, but rivals must overcome NVIDIA's entrenched CUDA software ecosystem to displace it. |
| Chain position | Builds and integrates the rack-level AI systems the cloud buildout ships in: cloud and networking products were 51% of 2Q26 revenue, NVIDIA Vera Rubin NVL72 was shown at COMPUTEX 2026, and the company said on 12 Aug 2026 that Vera Rubin racks were entering mass production in the third quarter. | Sits at the enterprise data and governance layer of the AI stack, and the AI exposure is explicit rather than incidental: the filing brands the product the 'AI Data Cloud,' lists AI as a product category, and put Snowflake Intelligence, Cortex Agents and a Managed MCP Server into general availability during the fiscal year. It is a buyer of hyperscaler compute and of third-party frontier models — 'strategic partnerships with foundational model providers deliver state-of-the-art models natively within Snowflake Cortex AI,' with stated 'model neutrality' — and a supplier of governed enterprise data and GPU-backed managed compute to AI applications built on top. | Upstream compute-platform supplier: NVIDIA sells full-stack data-center systems (GPU + Arm CPU + DPU + NVLink/InfiniBand networking + CUDA software) to cloud providers and enterprises, and relies on third-party foundry/assembly-test-packaging partners (e.g., SPIL, Amkor, Wistron, Fabrinet). Per the FY2026 10-K, several of its largest customers (hyperscalers such as Amazon, Alphabet, Microsoft) are simultaneously customers and emerging competitors developing internal accelerated-computing silicon. |
| Products (share / barrier) |
|
|
|
| Long-horizon vote | +0.13 at weight 0.20 · swarm neutral Editorial prior, not backtested. | +0.13 at weight 0.20 · swarm neutral Editorial prior, not backtested. | +0.42 at weight 0.20 · swarm bullish Editorial prior, not backtested. |