NextFin News - The high-stakes alliance between the world’s most valuable chipmaker and its most prominent AI startup has hit a significant roadblock. Five months after the September 2025 announcement of a non-binding letter of intent for Nvidia to invest up to $100 billion in OpenAI’s infrastructure, the deal has reportedly stalled, with both parties now walking back the scale of the commitment. According to Ars Technica, Nvidia CEO Jensen Huang recently clarified that the $100 billion figure was "never a commitment," while reports from Reuters indicate that OpenAI has been actively seeking alternative hardware to reduce its reliance on Nvidia’s GPUs for critical inference tasks.
The friction surfaced as OpenAI engineers reportedly expressed dissatisfaction with the speed of Nvidia chips in executing inference—the process of generating real-time responses for users. This performance bottleneck became particularly evident in OpenAI’s Codex tool, where the latency of GPU-based hardware hindered the development of "agentic" AI systems that require near-instantaneous feedback loops. In response to these technical hurdles, OpenAI has held discussions with competitors including AMD and specialized startups like Cerebras and Groq, aiming to diversify its hardware stack for approximately 10% of its future computing needs. While U.S. President Trump’s administration has emphasized maintaining American leadership in AI, this internal industry rift suggests that the path to dominance is becoming increasingly fragmented by specialized technical requirements.
The unraveling of this deal is not merely a financial disagreement but a reflection of the maturing AI lifecycle. For the past three years, the industry has been in a "training phase," where Nvidia’s H100 and Blackwell architectures were the undisputed gold standard for processing massive datasets. However, as models like GPT-5 and Gemini 3 move into mass-market deployment, the focus has shifted to "inference." McKinsey projects that the inference market could eventually be two to five times larger than the training market, reaching over $200 billion annually by 2028. In this new arena, the general-purpose nature of Nvidia’s GPUs—once their greatest strength—is becoming a liability compared to Application-Specific Integrated Circuits (ASICs) designed specifically for low-latency response generation.
The financial implications of this "fizzled" deal are profound. The original $100 billion plan was criticized by some Wall Street analysts as a form of "circular dealmaking," where Nvidia would provide the capital that OpenAI would then use to lease Nvidia’s own chips. By stepping back from this massive commitment, Huang may be signaling a more disciplined approach to capital allocation, especially as competition from Google’s Tensor Processing Units (TPUs) and Anthropic’s custom hardware intensifies. Huang’s private concerns regarding OpenAI’s "lack of discipline" and the rising threat from Google suggest that the era of blank-check investments in AI startups may be coming to an end.
Furthermore, the ripple effects extend to other infrastructure giants. Oracle, which has a $300 billion multi-year agreement to provide cloud capacity to OpenAI, saw its stock decline as investors questioned the stability of the broader AI ecosystem. Although Oracle issued a statement on February 2, 2026, asserting that the Nvidia-OpenAI friction has "zero impact" on its financial relationship, the market remains skeptical. The sheer scale of OpenAI’s infrastructure commitments—estimated at $1.4 trillion—contrasts sharply with its current revenue trajectory, creating a precarious "capital conveyor belt" that relies on constant, massive infusions of cash from hardware partners.
Looking ahead, the industry is likely to see a "Great Diversification." OpenAI’s recent commercial agreement with Cerebras for wafer-scale chips and Nvidia’s defensive $20 billion acquisition of Groq technology indicate that the battle for AI supremacy has moved from the data center floor to the silicon architecture itself. While Altman continues to publicly praise Nvidia as making the "best AI chips in the world," the strategic pivot toward specialized inference hardware is irreversible. For Nvidia, the challenge will be whether its new Rubin platform can close the performance gap in inference before its largest customers build their own silicon-independent futures. The fizzling of the $100 billion deal may well be remembered as the moment the AI bubble began to trade hype for hard-nosed engineering reality.
Explore more exclusive insights at nextfin.ai.