Nvidia’s earnings have become far more consequential than a routine corporate results report, with CNBC’s Jim Cramer warning that the chipmaker’s performance could influence sentiment across the stock market because of its central role in the global artificial intelligence buildout.

“Nvidia’s truly become anything but ordinary, it’s all-important, and it’s the ultimate battleground,” Cramer said Tuesday on CNBC’s “Mad Money.”

The comments came as Nvidia ended a seven-session losing streak, its longest since September 2022, just months before the launch of ChatGPT helped ignite the generative AI boom that transformed demand for Nvidia’s graphics processors and propelled the company into the ranks of the world’s most valuable businesses.

Nvidia’s shares closed Tuesday less than 10% below their record closing high of nearly $236 reached in May. The company is scheduled to report its results after the market closes Wednesday, with investors looking for evidence that the enormous spending on AI infrastructure by technology companies can continue to generate sufficient returns.

But the stakes extend well beyond Nvidia itself.

The company has become a critical supplier to the infrastructure supporting the AI industry, with its GPUs powering the training and inference of many of the most advanced models. Its fortunes are closely linked to data-center construction, networking equipment, high-bandwidth memory, advanced packaging and the capital expenditure plans of the world’s largest technology companies.

“Never before has there been a company with so many tentacles in so many segments of the economy,” Cramer said.

That interconnectedness has made Nvidia something of a barometer for the sustainability of the AI investment cycle. A strong report could reinforce the view that hyperscalers and other customers are still willing to spend heavily on computing capacity. A weaker outlook, however, could revive concerns that the extraordinary expansion in AI capital expenditure is approaching a point of diminishing returns.

Cramer remains bullish on Nvidia’s competitive position, even as competition intensifies.

OpenAI and other major AI companies are developing their own chips, while established semiconductor rivals are seeking to take market share from Nvidia. Customers are also exploring alternatives as they try to reduce their dependence on a single supplier and lower the cost of running increasingly large AI workloads.

Still, Cramer noted that Nvidia remains the industry standard.

“I say if you’re going to be part of the AI revolution, you have to go with the best,” he said.

The more difficult question for investors is whether Nvidia’s technological lead is sufficient to justify its valuation and the expectations embedded in its stock price.

Cramer said the company will be judged on more than revenue and earnings growth. Nvidia has increasingly become a proxy for the wider debate over whether the AI boom represents a durable technological transformation or an investment cycle that has become excessive.

“If the bears are right, this stock will tumble regardless of what it reports tomorrow,” Cramer said.

One area receiving increased scrutiny is Nvidia’s investment activity across the AI ecosystem. The company has invested in several businesses that are themselves customers or potential customers of AI infrastructure, prompting concerns among some investors about a circular flow of capital. The argument is that Nvidia can invest in AI companies, those companies can use the funding to expand their computing infrastructure, and some of that spending can ultimately return to Nvidia through purchases of its chips and systems.

Cramer rejected the criticism, saying that Nvidia is using its financial strength to accelerate the development of an ecosystem that ultimately expands the market for its products.

“Why shouldn’t Nvidia use its profits to help ensure that it remains on top?” he said.

That strategy also exposes the changing nature of Nvidia’s business. The company is no longer simply selling chips into an emerging market. It is now investing across the infrastructure, software, and startup ecosystem surrounding AI, seeking to bolster an ecosystem built around its computing architecture.

But several risks remain outside Nvidia’s direct control.

Data-center construction has encountered political and regulatory opposition in some markets, while shortages of high-bandwidth memory have constrained the supply chain. Nvidia also continues to face U.S. export restrictions limiting its ability to sell some advanced AI processors into China, one of the world’s largest technology markets.

Those restrictions create a challenge for Nvidia, which must simultaneously satisfy enormous demand from U.S. and other global customers while navigating Washington’s technology faceoff with China.

The company is also entering a more competitive phase of the AI chip market. AMD is expanding its accelerator business, cloud providers are developing proprietary processors, and AI companies such as OpenAI are pursuing alternatives that could eventually reduce their reliance on Nvidia.

Yet the central issue for investors is not whether Nvidia will face competition. It is whether the overall AI infrastructure market will continue expanding fast enough for Nvidia to maintain exceptional growth even as competition increases.

That makes Wednesday’s outlook potentially more important than the headline quarterly numbers.

Investors will be looking for indications of continued demand for data-center GPUs, the pace of deployment of new AI infrastructure, customer capital expenditure plans and the extent to which supply constraints are limiting sales. They will also be watching whether Nvidia can maintain its margins as customers gain more bargaining power and alternative processors become increasingly available.

The market’s reaction could therefore extend across the AI supply chain. Industry analysts say memory-chip manufacturers, semiconductor equipment companies, data-center operators and other firms benefiting from the AI infrastructure cycle could move in tandem with Nvidia.

The same applies to the broader equity market. Nvidia’s enormous market value means a significant move in its shares can materially affect major stock indexes and investor sentiment, particularly as concerns over expensive technology valuations continue to compete with optimism about AI-driven earnings growth.

Cramer’s warning captures the unusual position Nvidia now occupies in financial markets. Its earnings are no longer simply a measure of how one semiconductor company is performing. They have become a test of whether the extraordinary spending spree behind the AI revolution can continue.

“There’s no such thing as a nothing burger about the largest stock in the world,” Cramer said, explaining that every Nvidia result carries unusually high stakes for investors.

OpenAI CEO Sam Altman said he underestimated how quickly artificial intelligence would reshape businesses and displace established software products, acknowledging that companies have been far slower to change their habits than the technology’s rapid development initially suggested.

In an interview with David Senra published Sunday, Altman said he had expected the release of GPT-4 in 2023 to quickly create opportunities for new software companies and force businesses to reconsider the tools they used. Instead, customers have largely continued buying from familiar vendors and using established products.

“I think it means we’ve all been too ambitious on timelines,” Altman said. “People keep doing the same things they’re doing. They keep buying from the same company. They keep sort of wanting to use their tools in the same way.”

Altman described the slower transition as “positive in many ways,” offering a more cautious assessment of AI’s economic impact than some of the predictions that accompanied the generative-AI boom.

That matters for the technology industry because AI capabilities have advanced rapidly, but technological capability and economic adoption are not the same thing. Companies must change procurement processes, train employees, integrate new systems with existing infrastructure, and become comfortable relying on AI for business-critical tasks.

Those barriers can slow disruption even when a new technology appears capable of replacing an existing product.

OpenAI and other leading AI companies have spent years arguing that capable models could make businesses more efficient, allow smaller teams to compete with established companies, and automate substantial amounts of white-collar work.

Anthropic CEO Dario Amodei has gone further, predicting that AI could eliminate as much as half of entry-level white-collar jobs within five years.

Those expectations have already affected financial markets. Software-as-a-service companies came under heavy pressure in early 2026 as investors began questioning whether AI systems could reduce demand for traditional enterprise applications.

The selloff, dubbed the “SaaSpocalypse,” hit companies including Salesforce, Atlassian and Asana as investors considered the possibility that businesses could increasingly use AI tools to create customized software rather than purchasing standardized applications.

But Altman’s latest comments suggest that the disruption may take longer to materialize at scale. He compared the situation with the transition from physical video rental to streaming. Customers continued visiting Blockbuster even after Netflix had begun offering DVD rentals by mail, illustrating how established habits can survive even when a more convenient technology is already available.

“It was amazing to me that people still went to Blockbuster,” Altman said. “That is an example that has stuck in my head of like force of habit, and the way people do things and changing behavior is just much harder than the tech nerds realize.”

The comparison points to a central challenge for AI companies. Developing a model that can perform a task is only the first step. Convincing millions of people and thousands of companies to change how they perform that task can take considerably longer.

Enterprise customers, in particular, have reasons to move cautiously. Software is often deeply embedded in corporate workflows, data systems and compliance processes. Replacing an established application with an AI-based alternative can create operational and security risks even when the new system is technically more capable.

There is also an economic question about who captures the benefits of AI. A company may use AI to perform a task more efficiently without abandoning the software vendor that already provides its broader workflow. AI could therefore initially increase the productivity of existing products rather than immediately destroy them.

Altman acknowledged that this inertia exists even inside his own working habits.

He said he still manually works through his email inbox even though OpenAI’s Codex could automate more of the process. That example illustrates the gap between what AI can theoretically automate and what users actually choose to delegate. People may continue performing tasks themselves because they prefer existing routines, want to retain control, or simply have not developed new habits around AI.

The slower adoption cycle complicates some of the more aggressive assumptions embedded in AI valuations for investors. The technology may ultimately transform software, employment and corporate operations, but the timing of that transformation is increasingly difficult to predict.

For AI companies, the implication is equally significant. Technical progress alone may not be enough to produce rapid economic disruption. Distribution, integration, trust and changes in user behavior could determine how quickly AI moves from an impressive capability into a replacement for established products.

Altman’s comments therefore amount to a reassessment of the timeline rather than a retreat from the broader AI thesis. OpenAI still expects increasingly capable models to change how people and companies work. But the experience since GPT-4 has shown that technological disruption can move at two different speeds: AI capabilities can advance rapidly while the institutions and people expected to use them change much more slowly.

Nvidia’s near-monopoly over advanced artificial intelligence chips is coming under increasing pressure as OpenAI and other major technology companies develop custom semiconductors designed to reduce their reliance on the chip giant, analysts told CNBC.

OpenAI unveiled its first AI chip, Jalapeño, on Tuesday and said initial testing showed “industry-leading speed and efficiency.” Developed in partnership with Broadcom, the chip is designed primarily for AI inference, the process of running trained models to generate responses and perform tasks for users.

The development has become of interest because inference is becoming one of the fastest-growing sources of AI computing demand. As companies deploy AI agents and models to millions of users, the amount of computing required to operate those systems after training can become enormous.

Nvidia remains the dominant supplier of AI accelerators, with its GPUs powering much of the training and inference infrastructure used by leading AI companies and cloud providers. Its CUDA software ecosystem has also created a significant barrier to switching because developers have built years of tools and applications around Nvidia’s architecture.

But the emergence of custom chips threatens to weaken that advantage, particularly among the largest technology companies that have the scale and engineering resources to design their own silicon.

Adrien Sanchez, a technology analyst at Yole Group, said Jalapeño demonstrated that a chip designed by a major cloud or AI company can now compete with Nvidia’s Blackwell-class GPUs on inference efficiency.

“Nvidia still owns the vast majority” of AI compute and retains strong software ecosystem lock-in through CUDA, Sanchez said. But OpenAI’s chip represents a “threat to Nvidia’s inference margins, which is the field growing the most at the moment.”

Nvidia’s biggest vulnerability may not be an immediate loss of its overall AI chip leadership, but pressure on the economics of individual workloads.

OpenAI is one of Nvidia’s largest customers, buying huge volumes of GPUs to train and operate its AI models. If the company can shift a meaningful portion of inference workloads to its own chips, it could reduce its dependence on Nvidia and gain greater control over computing costs.

OpenAI said Jalapeño would enable faster responses, more responsive AI agents and more reliable access as demand increases. The chip is expected to be deployed in OpenAI’s infrastructure by the end of the year, and the company said it is already developing second- and third-generation versions.

That creates the possibility of a broader strategic shift. Instead of relying almost entirely on merchant GPUs, OpenAI could eventually operate a mixed infrastructure in which Nvidia hardware is used for workloads where its flexibility and performance provide the greatest advantage, while custom accelerators handle predictable, high-volume inference tasks.

Alexander Harrowell, senior principal analyst at Omdia, described Jalapeño as an “impressive achievement,” particularly in efficiency.

“In a large-scale deployment, this would save power, cooling, and power distribution infrastructure, and contribute a lot to their unit economics,” he said.

The savings could be substantial at the scale of OpenAI’s infrastructure. AI data centers consume enormous amounts of electricity, and the cost of cooling and distributing that power adds significantly to the cost of operating large clusters. A more efficient accelerator can therefore improve economics even if its headline computing performance is not dramatically higher.

Still, Jalapeño does not mean Nvidia’s dominance is about to disappear.

Fion Chiu, an analyst at TrendForce, said OpenAI’s custom chip could reduce its reliance on Nvidia for inference over time, but Nvidia GPUs are likely to remain important for computationally intensive workloads such as large-scale model training and frontier AI.

Nvidia retains advantages in programmability, performance, its software ecosystem, and the ability to support a broad range of workloads, Chiu said.

Benchmark comparisons also require caution.

Research firm SemiAnalysis tested Jalapeño after visiting OpenAI’s facilities and found that it delivered better performance per watt than Nvidia’s Blackwell architecture in nearly all of the scenarios it tested.

But SemiAnalysis said the comparison was “somewhat incomplete and unfair” because Jalapeño uses newer HBM4 memory. Nvidia’s forthcoming Rubin platform also uses HBM4, making Rubin a more appropriate comparison.

“Jalapeño is really competing against chips like Rubin that also use HBM4,” SemiAnalysis analysts said.

Nvidia’s Rubin systems are already beginning to ship to customers, while OpenAI still has engineering samples of Jalapeño, highlighting another challenge for OpenAI: bringing its custom architecture into large-scale production and deployment.

OpenAI is also far from alone in pursuing custom silicon.

Google has developed its own tensor processing units for AI training and inference, while Meta has committed to deploying custom AI chips using Broadcom technology. Amazon has developed its Trainium family of AI accelerators, with Anthropic committing to more than $100 billion of spending on AWS technology over the next decade, including current and future generations of Trainium.

The shift is becoming notable because hyperscalers account for a substantial share of global AI infrastructure investment.

Omdia expects custom application-specific integrated circuits, or ASICs, to surpass GPUs in unit volume by 2028, although GPUs are likely to remain ahead in revenue because they are substantially more expensive.

“This is the biggest competitive threat to NVIDIA,” Harrowell said, arguing that about half of AI infrastructure capital expenditure comes from hyperscale cloud providers that already have custom-chip programmes or have the resources to develop them.

The economics explain why the hyperscalers are willing to invest heavily in semiconductor design. Building an AI chip requires enormous upfront engineering costs, but at sufficient scale the savings from owning the architecture can outweigh the cost of development.

Custom silicon can also be optimized for a company’s particular models and workloads rather than being designed to serve the broadest possible customer base. That could gradually erode one of Nvidia’s traditional advantages. Nvidia sells general-purpose accelerated computing platforms that can support a wide range of AI workloads. Hyperscalers, by contrast, can design chips around their own software stacks, models and data-center architectures.

The competitive field is widening beyond the major technology companies as well. Startups including Cerebras, SambaNova, D-Matrix, Etched and Fractile are developing specialized AI processors targeting different parts of the AI computing market.

For Nvidia, however, the biggest issue may be the changing relationship with its largest customers.

OpenAI has been one of the biggest single consumers of Nvidia GPUs. If it succeeds in deploying Jalapeño at scale, the company could gain bargaining power over future Nvidia purchases while simultaneously lowering its dependence on the supplier.

Sanchez said Jalapeño “raises the stakes for Nvidia’s largest customer relationship specifically.” That does not necessarily mean OpenAI will stop buying Nvidia GPUs. More likely, the AI industry is moving toward a heterogeneous computing model in which Nvidia GPUs, custom ASICs and other accelerators coexist.

Nvidia’s challenge will be maintaining its technological lead and software advantage while its largest customers increasingly have an economic incentive to develop alternatives. The bigger change is that AI chip competition is shifting from a market dominated by a single merchant-chip supplier toward one in which the largest AI companies increasingly control part of their own semiconductor stack.