Nvidia Freezes OpenAI’s $100B ‘Commitment’; Huang Cites ‘Discipline’ Issues
Nvidia CEO Jensen Huang clarifies the $100B figure was an ‘invitation,’ not a promise, as technical disagreements over inference hardware surface.
The artificial intelligence sector’s most critical alliance is showing structural cracks. Nvidia CEO Jensen Huang shattered expectations in Taipei on Monday, clarifying that the widely reported $100 billion investment plan for OpenAI was “never a commitment” but merely an “invitation” to invest up to that amount over time. The clarification sent Nvidia (NVDA) shares sliding 3.5% in premarket trading to ~$187, while dragging crypto AI proxies like Bittensor (TAO) down 5%.
The ‘Vendor Financing’ Trap
Huang’s retraction exposes a deeper institutional fear: circular financing. The proposed deal structure, where Nvidia injects capital that OpenAI immediately recycles into purchasing Nvidia GPUs, raised red flags among analysts who likened it to “vendor financing” schemes. A direct cash injection of that magnitude would have artificially inflated Nvidia’s own revenue guidance, a risk Huang is now publicly mitigating.
“They invited us to invest up to $100 billion… but we will invest one step at a time.” — Jensen Huang, Taipei, Feb 2, 2026
Privately, the friction is more abrasive. Huang has reportedly criticized a “lack of discipline” in OpenAI’s business strategy, signaling that the chip giant is wary of pouring liquidity into a partner burning cash on unproven infrastructure scales. OpenAI is now scrambling to fill the capital gap, reportedly opening talks with Amazon and SoftBank.
The Hardware Rift: Inference Latency
Beyond the balance sheet, a technical schism is forming. Reports indicate OpenAI is unsatisfied with Nvidia’s current H100/Blackwell architecture for inference tasks. While Nvidia dominates training, OpenAI’s specific needs for high-speed reasoning require massive on-chip SRAM to reduce latency, a feature where competitors like Groq and Cerebras currently hold architectural advantages.
This hardware divergence complicates the narrative for crypto AI projects like Render (RENDER) and Fetch.ai (FET), which rely heavily on the assumption of Nvidia’s monopolistic utility. If the AI stack fragments between training (Nvidia) and inference (Custom/ASIC), the tokenomics of decentralized GPU networks may need to be repriced.
Market Reaction
The “AI Monolith” trade is unwinding. Bittensor (TAO), often traded as a high-beta proxy for institutional AI sentiment, failed to hold the $200 support level, dropping 5.1% to ~$197. Traders should watch the $185 level closely; a break below this technical floor could signal a broader capitulation in the AI-crypto narrative.
