Nvidia's AI Moat: What's Priced In After the OpenAI Reassurance?

The market's reaction to Sam Altman's reassurance was a textbook case of "buy the rumor, sell the news." When Altman posted on X on Tuesday, reaffirming OpenAI's commitment to Nvidia as a "gigantic customer for a very long time," the stock's premarket gain was muted at just 0.59%. That tepid pop suggests the market had already priced in the risk of OpenAI relationship tension. The news was good, but it was not new.

This fits a broader pattern of underperformance. Over the past 20 days, Nvidia shares have been down 1.7%, and year-to-date, the stock is essentially flat. The reassurance came against this backdrop of stagnation, indicating that the OpenAI risk had been a persistent overhang for weeks, not a sudden shock. In other words, the expectation gap was already closed.

The setup was clear even before Altman's post. Over the weekend, CEO Jensen Huang had already dismissed any tension as "nonsense" and reaffirmed his support. The market had been ahead of the news, digesting the CEO's comments and the underlying reports of OpenAI exploring alternatives for inference chips. Altman's Tuesday post was a formal, public confirmation of what the stock had already discounted. For a "buy the rumor" trade to work, the news must be better than expected. In this case, it was simply what was already priced in.

The Reality Check: Inference as the New Battleground

The OpenAI reassurance was a PR win, but it sidesteps the core business risk. The market's focus has shifted from training to inference, and that's where Nvidia's dominance is being tested. The company remains unchallenged for training massive models, but inference-the process of generating a response after a user asks a question-has become a separate battleground. Here, speed is everything for user experience, and OpenAI is dissatisfied.

Sources say OpenAI is not satisfied with the speed at which Nvidia's hardware can spit out answers for specific tasks, particularly software development and AI communicating with other software. The problem lies in memory architecture. For inference, chips spend more time fetching data than calculating, creating a bottleneck with traditional GPUs that rely on external memory. OpenAI needs hardware with large amounts of embedded SRAM, a design that can drastically cut response latency.

This has led OpenAI to explore alternatives from AMD, Cerebras, and Groq. The company is seeking new hardware that would eventually provide about 10% of its inference computing needs in the future. That may seem like a small slice, but it represents a direct challenge to Nvidia's total cost of ownership and a potential foothold for competitors in a critical workload.

Nvidia's response was swift and strategic. In a move that effectively blocks one alternative path, the company struck a $20 billion licensing deal with Groq. This isn't just a partnership; it's a calculated effort to neutralize a key competitor in the inference space. The deal underscores that Nvidia sees inference as the next frontier for its dominance.

The bottom line is that the expectation gap here is wide. The market had priced in OpenAI as a loyal, massive customer for training. The new reality is a more complex, competitive landscape for inference. The $100 billion investment deal between the two companies has stalled for months, a clear sign that the relationship is under strain. For now, Nvidia holds the field, but the battle for the next phase of AI hardware is being fought on the inference battlefield.

The most telling sign of friction isn't the public reassurances, but the stalled deal. The proposed investment, announced in September, was expected to close within weeks. Months later, negotiations are still ongoing. This delay is a major strategic reset, signaling that the relationship is under significant strain. For a partnership built on Nvidia's hardware and OpenAI's software, a deal that has been delayed for months is a clear red flag.

The friction centers squarely on inference speed for specific tasks. OpenAI is dissatisfied with the response time of Nvidia's latest chips for workloads like software development and AI communicating with other software. The problem is architectural: traditional GPUs rely on external memory, creating a bottleneck during inference where chips spend more time fetching data than calculating. This directly challenges Nvidia's performance leadership in the new critical phase of AI.

The strategic implication is a fundamental shift in the competition. As the AI industry moves toward inference, the battleground is no longer just about raw training power. The next phase will be defined by performance per dollar and response latency. OpenAI's search for alternatives, targeting about 10% of its future inference computing, is a test of this new paradigm. It shows a customer willing to diversify for better speed and cost efficiency, even for a major partner.

For Nvidia, the $20 billion licensing deal with Groq was a preemptive strike to neutralize a key alternative. But the stalled $100 billion investment deal with OpenAI itself reveals a deeper vulnerability. The expectation gap here is wide: the market had priced in a seamless, multi-billion dollar partnership. The reality is a complex negotiation over hardware performance and strategic alignment. This sets the stage for a more competitive, performance-driven hardware market where Nvidia's dominance is no longer guaranteed.

Catalysts & What to Watch

The expectation gap is now defined by forward-looking events. The next major catalyst is Nvidia's upcoming earnings report, scheduled for February 25. This will be a critical test. Investors need to see if the company addresses inference-related revenue or provides guidance that clarifies the trajectory for this new, competitive front. The market has priced in OpenAI loyalty, but the reality is a complex negotiation over hardware performance. The earnings call is where Nvidia must either confirm its dominance in inference or signal that the competitive pressure is starting to impact its growth forecast.

Beyond the quarterly print, watch for any updates on the stalled $100 billion investment deal. Its prolonged delay is a major red flag. Any news-whether a resolution, a new timeline, or a breakdown-will be a direct signal of the partnership's health. For now, the silence speaks volumes. The market consensus is that the deal is in trouble, and any movement will reset expectations.

Equally important is monitoring for new partnerships or deals that OpenAI or other major AI players strike with alternative chipmakers. The evidence shows OpenAI is already exploring AMD, Cerebras, and Groq. While its stated goal is to source about 10% of its future inference computing from these alternatives, that's a foothold. Any public announcement of a new hardware partnership would be a tangible sign that Nvidia's moat is being tested in the inference phase.

The bottom line is that the near-term growth trajectory hinges on guidance for inference chip demand versus training. Nvidia's valuation, trading at a forward P/E of nearly 50, assumes continued dominance. The company must now prove that its performance leadership translates into sustained revenue growth in this new battleground. Until then, the expectation gap remains wide.