Nvidia earnings discussion keeps focus on enterprise AI momentum, Dan Ives says Microsoft, Google and Amazon could lead

Nvidia’s earnings have helped keep the market’s “AI Jenga puzzle” intact, according to Dan Ives of analyst firm Wedbush, who discussed the outlook in an interview aired on CNBC. The core message was that Nvidia’s momentum in supplying accelerated computing is only one piece of a broader enterprise rollout, and that the next major moves may come from large platform owners and cloud ecosystems rather than from Nvidia alone.

Ives’ comments tied the immediate post-earnings narrative to what he described as the stability of the overall AI stack as it scales. In his view, the AI market does not move as a single unit, but through a series of connected decisions by enterprises, including where AI is implemented, how data is managed, and which software layers are deployed. That sequencing, he suggested, is what makes the “Jenga” framing useful for investors tracking how the pieces fit together.

While Nvidia remains the key supplier at the center of the buildout, Ives argued that Microsoft, Google and Amazon could be positioned to “lead the next trade” given their roles in enterprise technology and cloud platforms. The implication for markets is that spend patterns and product capabilities at those firms can influence how quickly customers expand AI workloads beyond early experimentation.

The CNBC interview also broadened the list of possible beneficiaries beyond hyperscalers and hardware suppliers. Ives pointed to Palantir and cybersecurity companies, specifically naming CrowdStrike, as potential gainers if enterprise AI adoption broadens. His argument was not that Nvidia’s earnings automatically translate into immediate revenue for these companies, but that changes in the pace of enterprise adoption can open demand for additional software, analytics, and security tooling around AI-powered operations.

From a business perspective, the logic is straightforward but the timing is often uneven. When customers deploy AI in operational environments, they generate new data flows and system dependencies that can stress existing governance and threat detection. That creates conditions where cybersecurity vendors can find new use cases, particularly as organizations connect model outputs to business processes, endpoints, and cloud services.

Palantir, meanwhile, represents a different part of the enterprise stack. Rather than selling infrastructure, Palantir is commonly viewed as an applications and data integration layer for decision-making workflows. In an environment where enterprises expand AI usage, products like those can face an incremental demand tailwind if they are able to integrate AI-driven analytics into existing operations.

Notably, the post does not provide specific financial metrics from Nvidia’s results, nor does it detail any changes in forward guidance, new product launches, or quarter-by-quarter segment trends. It focuses on the strategic framing and on which categories of companies may see relative benefit if enterprise adoption accelerates. As a result, investors looking for earnings detail may need to consult Nvidia’s official disclosures for numbers and any forward-looking statements.

What to watch next is whether the next phase of AI scaling is reflected in customer spending indicates and in platform-level product rollouts at major cloud and enterprise software providers. If Microsoft, Google and Amazon are indeed positioned to lead the next wave, markets may look for evidence in cloud services usage, enterprise AI deployment announcements, and updates to security and applications ecosystems that depend on accelerated computing and AI inference.

In addition, the market will likely track whether security and analytics vendors can convert broader AI interest into measurable demand. The relationship between enterprise AI expansion and incremental business outcomes for companies such as CrowdStrike and Palantir can depend on how customers operationalize AI, the pace of integration projects, and the effectiveness of security controls tailored to AI-driven workflows.