Jensen Huang’s Visit to Korea (Part 3): The NVIDIA CUDA Barrier and the Big Tech Counterattack

Let's learn more about Nvidia CEO Jensen Huang's visit to South Korea

— Countermeasures from Musk, China, and Europe, alongside Opportunities and Crises for India and South Korea — In June 2026, the afterimages left by NVIDIA CEO Jensen Huang’s visit to South Korea were nothing short of spectacular. His consecutive meetings with the heads of Korea’s top conglomerates—including Samsung Electronics, SK Hynix, Naver, and Hyundai Motor—seemed to prove just how consolidated the "AI infrastructure alliance" built by NVIDIA truly is. Behind the glitz and glamour, however, global Big Tech firms and major nations are hard at work seeking massive alternatives to avoid being trapped under NVIDIA’s suffocating dominance. Because this technological monopoly's fortress is so robust, it is difficult to definitively predict the downfall of the NVIDIA empire in the short term. Nevertheless, competitors outside the fortress walls are solving their long-term survival equations by weaponizing their own "captive consumer markets." Let's look into the flip side of the alliance shown during Jensen Huang’s visit: the true nature of the monopoly and the practical countermeasures being taken by an increasingly diversified global tech ecosystem. 1. The Reality of NVIDIA’s Monopoly: A "Semi-Open Software Lock-In," Not Hardware Many believe NVIDIA dominates the market simply because its Graphics Processing Units (GPUs) offer overwhelming performance. However, the truly terrifying barrier to entry lies not in the hardware, but in the software ecosystem. An 80–90% Share of the AI Data Center GPU Market: From Blackwell , which is essential for LLM (Large Language Model) training and inference, to the next-generation Vera Rubin platform, NVIDIA’s AI accelerators (dedicated AI computing semiconductor packages) command absolute dominance over the market. The Software Shackle, "CUDA": This is the core pillar of NVIDIA’s monopoly. Distributed for free in 2006, the CUDA development platform has become the "default standard" used by AI developers and researchers worldwide to write code over the past 20 years. ♣ The Origin of the Name "CUDA" CUDA stands for Compute Unified Device Architecture . True to its name, it embodies Jensen Huang’s obsession: to allow software developers to universally integrate and control the powerful computing capabilities of GPUs using standard programming languages (C/C++) , without needing complex graphic commands. The Trap of a Seemingly Low-Barrier "Semi-Open Structure": CUDA is not a completely closed system. It features a "semi-open structure" that integrates seamlessly with global open-source AI frameworks like PyTorch and TensorFlow. By providing convenience to developers, NVIDIA has deeply rooted them into its ecosystem. Consequently, this functions as a highly sophisticated platform lock-in strategy , making it impossible for the code to run without NVIDIA hardware. The Real Core is TCO (Total Cost of Ownership): The reason enterprises cannot easily switch to alternative chips from AMD or Intel is not merely the price of the chips. Migrating to another company's hardware requires rewriting existing CUDA-based code, incurring years of development time and astronomical engineering costs. When factoring in electricity bills, operational stability, and talent recruitment costs, looking at it through the lens of TCO invariably leads companies to the conclusion that sticking with NVIDIA is the most efficient choice. 2. The Counterattack of Giants with "Captive Markets": Vertical Integration and Hybrid Strategies The reason the anti-NVIDIA movement is so formidable in the long run is that the entities seeking alternatives possess not only chip design capabilities, but also massive downstream industries and domestic markets capable of immediately absorbing 100% of the chips produced for their own services or nations. While a short-term victory may be elusive, this is the backdrop that enables a long-term war of attrition. ① Elon Musk: A Hybrid Strategy of "Dojo" and Infrastructure Alignment Elon Musk is pursuing vertical integration by leveraging a guaranteed captive market where proprietary chips can be deployed immediately: Tesla (autonomous driving), xAI (the AI 'Grok'), and SpaceX (space and satellites). ♣ The Origin of the Name "Dojo" Dojo (道場) means a place to practice martial arts in Japanese. Musk personally named the project to signify a venue where artificial intelligence for Full Self-Driving (FSD) software is "honed and trained through grueling practice into a martial arts master," utilizing the immense volume of driving video data collected from Tesla vehicles worldwide. A Realistic Alternative, the Hybrid Route: Tesla has developed and deployed its proprietary AI supercomputer chip, the "D1" (Dojo chip), optimized for video analysis . However, given NVIDIA’s ongoing hardware hegemony, Musk is adjusting the pace of the Dojo project to balance proprietary chip development with the sheer scale of NVIDIA's infrastructure. By utilizing massive NVIDIA GPU clusters simultaneously, Tesla is executing a hybrid strategy to calibrate the timing of its ultimate independence. This is a clever approach that leverages NVIDIA as a foil rather than excluding it entirely. ② China: Achieving "100% Domestic Self-Sufficiency" via Policy Environments Faced with blocked access to NVIDIA chips due to strict U.S. regulations on advanced equipment, China is utilizing state-led industrial policies and regulatory environments to heavily nudge its domestic Big Tech firms toward adopting homegrown AI chips, such as Huawei’s Ascend series. Baidu, Alibaba, and Tencent are aligning with government directives to complete an independent domestic value chain: "Domestic Chip Production - Domestic AI Model Training - Domestic Service Consumption." ③ Europe: Dismantling Monopoly Walls through Regulation and "Open Source" Lacking advanced proprietary chip manufacturing capabilities, Europe combines its regulatory power to check corporate monopolies with the open-source ecosystem. Through policy and research funding, Europe actively promotes technical projects aimed at reducing reliance on CUDA, such as Triton (an open-source compiler led by OpenAI) and OpenXLA (driven primarily by Google). This sets the stage for developers to easily run AI code on AMD or Intel chips instead of NVIDIA hardware. 3. South Korea’s Semiconductor Stature: A "Vertical Integration" Plan B Weaponizing Manufacturing Prowess During Jensen Huang’s visit, NVIDIA hinted at diversifying its partnerships, even suggesting that Samsung Foundry manufacture next-generation inference semiconductors (such as Groq chipsets). What deserves attention here is that because South Korea possesses world-class semiconductor manufacturing and foundry capabilities, Korean enterprises have the structural weight to attempt vertical integration and independent ecosystems that link directly to their own consumer touchpoints , much like Musk or China. LG Electronics: In-House Domestication of Home Appliances and Robotics Centered on On-Device AI Inference LG Electronics has begun developing and embedding its own next-generation AI chipsets into smart appliances and robotic vacuum cleaners. By domesticating "On-Device AI" semiconductors—which infer and control human behavior directly inside the appliances without relying on massive cloud servers (NVIDIA infrastructure)—LG is lowering its dependence on NVIDIA and establishing a proprietary product ecosystem. This strategy is viable because LG possesses a massive captive market in its own home appliances. Samsung Electronics: Hyperscaler Alliances and Building Independent AI Factories While acting as a critical pillar supplying High Bandwidth Memory (HBM) to NVIDIA, Samsung Electronics is simultaneously carving out an alternative path in its foundry business. It is securing orders for custom ASICs (Application-Specific Integrated Circuits) from hyperscalers like Google, Amazon, and Meta, who possess massive workloads of their own. Furthermore, Samsung is executing a sophisticated multi-vendor strategy : it is transforming its own semiconductor fabrication plants into NVIDIA chip-based "AI Factories" to maximize manufacturing efficiency, while concurrently diversifying its foundry client portfolio. 4. The Survival Equation for SMEs: Niche Markets vs. Accelerated Dependency As corporate giants carve out workarounds and pursue vertical integration using their own consumption engines, two contrasting phenomena are unfolding simultaneously within the domestic and international semiconductor equipment, materials, components (SMC), and design house SME ecosystems. Opportunity: The Dawn of a Diversified Niche Market As cracks appear in the monolithic NVIDIA ecosystem and hyperscalers aggressively develop custom ASICs—coupled with Korean conglomerates domesticating on-device chips—the value of specialized IP (Intellectual Property) firms and boutique design houses is soaring. This is driven by an explosion in demand for "tailored and customized chip optimization" that does not need to conform to NVIDIA standards. In particular, India’s expansion into the outsourced semiconductor assembly and test (OSAT) sector via the state-driven India Semiconductor Mission (ISM) is providing a lucrative new revenue stream for Korean SMC SMEs looking to diversify away from China. Crisis: Deepening Monopolistic Subjugation to Giant Ecosystems Conversely, for resource-strapped micro-SMEs and startups, this transition presents a double-edged sword that could accelerate their subjugation to monopolistic ecosystems. While large conglomerates can afford multi-vendor strategies or vertical integration backed by massive capital and captive markets, small enterprises cannot even produce prototypes unless they adopt the CUDA ecosystem or the cloud infrastructure built by hyperscalers. There is a growing concern regarding structural polarization, where SMEs unable to afford platform migration costs become permanently entrenched as "subcontracted ecosystems" under a handful of tech giants. 5. India’s Ambition: Starting with OSAT and Pivoting toward a "Proprietary Model" Scenario India represents another critical variable in this complex, high-stakes equation. Currently, India is avoiding reckless entry into the ultra-fine front-end processes or advanced chip design segments, where the technological gap remains wide. Instead, weaponizing government subsidies, it is scaling up its presence in legacy fabs and OSAT ecosystems —the foundational infrastructure of the global semiconductor supply chain. However, India’s ultimate goal is by no means restricted to remaining at the bottom of the supply chain. India boasts a powerful talent pool that accounts for a substantial portion of global semiconductor design engineers, backed by the world's largest domestic consumer market. While it is currently cultivating manufacturing know-how and nurturing its downstream ecosystem by attracting packaging plants, the moment this manufacturing infrastructure matures, India harbors the latent potential to merge its abundant design talent with its massive domestic market to pivot into an "indigenous AI chip and proprietary model production" powerhouse at any time. Closing Thoughts Triggered by Jensen Huang’s visit to South Korea, this three-part series has explored the inner workings of the AI and semiconductor markets. This domain goes far beyond the boundaries of a specific tech sector; it is profoundly critical because it directly charts the future roadmap of our entire industrial landscape. The challenge is that the pace of change defies imagination. Because new theories and architectures pour out before foundational principles can even be fully institutionalized, actual field operations are struggling to keep up with standard educational curriculums. Realistically speaking, it is next to impossible for average individuals, SMEs, or even individual nations to seize and control the leadership of this global ecosystem. Even if it feels detached from your immediate reality today, AI infrastructure is already bleeding into the veins of every industry and daily routine. We do not have the luxury of wasting time debating in binary terms whether NVIDIA's fortress will hold or if its rivals' vertical integration will succeed. Ultimately, the best we can do right now is yield to this colossal technological wave, read the layout of a thoroughly diversified, "every-man-for-himself" playing field, and smartly exploit the niches most advantageous to us. If you cannot stop the wave, the ability to surf it becomes your sole strategy for survival.