Dan Ives Names 5 Tech Stocks for 2027, Including CrowdStrike

Dan Ives, the Wedbush Securities technology analyst known for his high-profile stock calls, has named Nvidia, Microsoft, Palantir, Apple, and CrowdStrike as his top five picks heading into 2027. He argues that investors still underestimate a $4 trillion wave of artificial intelligence spending that he believes will reshape the technology sector over the next several years. The five stocks have taken very different paths in 2026, which makes the list a useful map of where the AI trade actually stands.

The Five Picks and Their Very Different 2026 Paths

The five names have traveled very different roads this year. CrowdStrike, the cybersecurity firm, has more than doubled since the start of 2026, making it the standout performer on the list by a wide margin. Microsoft and Palantir have each posted strong gains as well, though their paths reflect different parts of the AI trade, one through cloud and software subscriptions and the other through data analytics contracts. Nvidia and Apple, the two largest companies in the group by market value, round out a list that spans the full stack of AI spending rather than betting on a single theme.

The divergence matters because it shows the AI trade is no longer monolithic. A year or two ago, much of the sector's gains were concentrated in chipmakers and a handful of mega-cap names. Now the spending has spread outward, reaching software companies, security providers, and platform businesses that monetize AI in less obvious ways. Ives' list captures that broadening, even as one member of the group has pulled far ahead of the others.

What is the $4 trillion AI spending wave?

At the center of Ives' argument is a simple claim: the total pool of money flowing into artificial intelligence is far larger than most investors assume. The $4 trillion figure represents his estimate of cumulative spending across enterprise software, cloud infrastructure, data centers, and the chips that power them. It is not a forecast of any single company's revenue but a view of the entire budget line that governments and corporations are constructing around AI, and he believes that budget will keep growing as use cases multiply.

That budget flows through every layer of the technology stack.

The money starts with hardware. AI training and inference run on specialized processors, which is why chipmakers sit at the front of the spending chain. From there it moves to cloud providers that rent out computing capacity to enterprises building AI applications. Software companies then layer AI features on top of products customers already pay for, converting the same shift into subscription revenue. Finally, security firms protect the endpoints, networks, and cloud workloads that AI adoption creates more of. Each layer captures a slice of the same budget, which is why one analyst's list can span five companies that barely compete with each other.

Why does Ives say investors still underestimate AI?

Ives has built his reputation on bold, specific calls, and his core argument is that the market keeps treating AI as a story about a few chip companies rather than a broad reallocation of technology budgets. When enterprises shift spending toward AI, he argues, the beneficiaries extend well beyond the companies that sell the hardware. Software, security, and cloud platforms all capture recurring revenue from the same shift, and that revenue tends to be stickier and more predictable than hardware cycles, which have historically swung between boom and bust.

Skeptics counter that much of this is already priced in after a big run.

The bear case on the group is straightforward. After sharp gains, valuations leave little room for disappointment, and any slowdown in enterprise AI budgets could compress multiples across the entire sector. Bulls respond that AI spending is still early in its adoption curve, pointing to the gap between announced enterprise pilots and actual production deployments. That gap, they argue, is where the next several years of growth come from.

How do these five stocks fit the bigger tech trend?

Each company on the list monetizes AI at a different layer. Nvidia designs the processors that train and run large AI models, making it the most direct beneficiary of data center buildouts around the world. Microsoft sells cloud computing through Azure and embeds AI across its Office software suite, giving it exposure to both infrastructure spending and enterprise subscriptions. Palantir builds data analytics platforms used by governments and large corporations to deploy AI on their own proprietary data. Apple reaches AI through its massive installed base of devices, where new features can drive upgrade cycles among hundreds of millions of users. CrowdStrike protects the endpoints and cloud workloads that AI adoption creates more of, a niche that grows automatically as companies digitize further.

The common thread is recurring revenue tied to AI budgets rather than one-time hardware sales.

Together the five companies cover the full chain of AI value creation, from the chips that make it possible to the applications that make it useful. That breadth is deliberate. Rather than concentrating the list in one part of the stack, Ives has assembled a portfolio that captures spending at every stage, so that a slowdown in any single layer does not sink the entire thesis.

What history says about concentrated AI bets

Concentrated bets on technology spending waves have a mixed track record. The late 1990s internet boom produced enormous winners but also destroyed companies that were simply too early or too leveraged. The cloud computing shift of the 2010s rewarded investors who stayed with the theme through volatility, but punished those who bought at peak valuations and sold at the first sign of slowing growth. The current AI trade shares features of both: a genuine shift in how companies spend money, combined with valuations that already reflect a great deal of optimism.

Patience has historically been the difference between the winners and the casualties.

The lesson most often cited by long-time technology investors is that spending waves of this magnitude rarely move in a straight line. Budgets get reallocated, priorities shift, and individual companies can lose share even as the market grows. That is why diversification across layers, rather than concentration in a single name, has historically been the more durable way to capture a broad technology transition.

What to watch next for AI tech stocks

The near-term catalysts are concrete and dated. Earnings seasons over the coming quarters will show whether enterprise AI budgets are actually converting into reported revenue at the companies that sell into that spending. Hyperscalers, the large cloud providers, disclose their capital spending plans every quarter, and those numbers set the tone for the entire AI supply chain. Any sign that companies are pulling back on data center construction would hit the sector's most crowded trades first.

Other markers worth tracking include enterprise contract announcements, government AI procurement budgets, and the pace at which AI features move from pilot programs to paid deployments. Each of these signals whether the $4 trillion spending wave is materializing on schedule or slipping to the right. The companies on Ives' list report on different schedules, so the picture will emerge in pieces rather than all at once.

The next earnings cycle will test whether the $4 trillion thesis holds up.