Wall Street’s most vocal technology bull is making a bold call: the artificial intelligence trade is far from over, and the next massive wave of spending is about to shift from hardware and infrastructure to the software companies that will define the next decade of enterprise computing. Dan Ives, senior equity analyst at Wedbush Securities, has been among the most prominent voices arguing that AI represents a generational investment cycle — and he now sees the emergence of a software-led spending surge that could reshape the fortunes of dozens of publicly traded companies.
According to a report from MSN, Ives has stated that “future AI tech winners are being built” right now as this software-led spending wave looms on the horizon. His thesis rests on a straightforward but powerful observation: the first phase of the AI boom enriched chipmakers and cloud infrastructure providers, but the second phase — the monetization layer — will be dominated by software firms that can translate raw computing power into enterprise applications, productivity tools, and autonomous decision-making systems.
From Chips to Code: The AI Investment Cycle Enters Its Second Act
The first leg of the AI investment cycle was defined by extraordinary capital expenditure. Nvidia became the poster child of the boom, with its GPU shipments powering data centers from Virginia to Singapore. Hyperscalers like Microsoft, Amazon, Alphabet, and Meta collectively committed hundreds of billions of dollars to building out AI infrastructure. But Ives argues that the real returns on those infrastructure investments will accrue to the software companies that sit on top of the hardware stack — the firms building the applications, platforms, and services that enterprises actually pay recurring subscription fees to use.
This is not a contrarian view, but Ives is among the first major analysts to put a specific dollar figure on the opportunity. He has projected that AI-driven software spending could approach $2 trillion over the coming years, a figure that dwarfs even the most optimistic estimates from just 18 months ago. The logic is that every enterprise that has invested in AI infrastructure — or that accesses it through cloud providers — will need software to operationalize those investments. That means demand for AI-enhanced cybersecurity, customer relationship management, data analytics, enterprise resource planning, and developer tools is set to accelerate sharply.
The Names Ives Is Watching — and Why They Matter
Ives has been particularly bullish on a handful of companies he sees as positioned to capture outsized share of this software spending wave. Microsoft remains his top pick, given its deep integration of OpenAI’s models across its product portfolio, from Copilot in Office 365 to Azure AI services for enterprise developers. He has maintained an Outperform rating on Microsoft and has repeatedly described the company’s AI strategy as the most comprehensive in the industry.
Beyond Microsoft, Ives has pointed to Palantir Technologies, ServiceNow, Salesforce, and CrowdStrike as companies with significant AI tailwinds. Palantir, in particular, has drawn his attention because of its growing presence in both government and commercial AI deployments. ServiceNow’s workflow automation platform is increasingly being embedded with AI capabilities that drive enterprise efficiency. CrowdStrike, meanwhile, is using AI to enhance threat detection and response in cybersecurity — a sector where spending is essentially non-discretionary for large organizations.
Why the Bears May Be Underestimating the Spending Trajectory
Skeptics have questioned whether the AI spending boom can sustain itself, pointing to the massive capital outlays by hyperscalers and asking when — or whether — those investments will generate adequate returns. Some analysts have drawn comparisons to the dot-com bubble, warning that expectations have outpaced reality. But Ives has pushed back forcefully against this narrative. In recent public appearances and research notes, he has argued that the current AI cycle is fundamentally different because it is being driven by enterprise demand rather than speculative consumer adoption.
The distinction matters. During the late 1990s, much of the technology spending was driven by companies with no revenue, no business model, and no clear path to profitability. Today, the AI spending wave is being led by some of the most profitable companies in history — firms like Microsoft, Apple, and Alphabet that generate hundreds of billions in annual revenue and have the balance sheets to sustain multi-year investment cycles. Ives has noted that enterprise AI adoption rates are running ahead of internal projections at several major software companies, suggesting that demand is not merely hype but is backed by genuine willingness to pay.
The Tariff Wildcard and Its Surprising Implications for Software
One factor that has added complexity to the outlook is the ongoing trade policy uncertainty under the current administration. Tariffs on semiconductors and hardware components have created headwinds for some segments of the technology sector, particularly companies with significant manufacturing exposure in Asia. But Ives has argued that software companies are relatively insulated from tariff risk because their products are delivered digitally, with minimal exposure to physical supply chains.
In fact, Ives has suggested that tariff-driven uncertainty could actually accelerate the shift toward software spending. As enterprises look to reduce costs and improve efficiency in an uncertain macroeconomic environment, AI-powered software tools that automate workflows, reduce headcount needs, and optimize operations become more attractive, not less. This dynamic creates a counterintuitive tailwind for the software sector even as other parts of the economy face pressure. Recent reporting from CNBC has highlighted how tech stocks have rallied on trade deal optimism, further supporting the thesis that the sector has room to run.
Enterprise Adoption Is Accelerating Faster Than Expected
Earnings reports from the most recent quarter have provided concrete evidence that AI adoption is gaining momentum across the enterprise sector. Microsoft reported that its Azure AI services revenue grew significantly, with management noting that demand continues to outstrip available capacity in some regions. Salesforce has highlighted growing adoption of its Einstein AI features among enterprise customers. ServiceNow’s CEO has described AI as the most significant product cycle in the company’s history.
These are not abstract projections — they are real revenue numbers being reported by public companies to their shareholders. Ives has seized on this data to reinforce his bullish thesis, arguing that Wall Street consensus estimates for many software companies remain too conservative. He has suggested that earnings revisions for AI-exposed software names could move meaningfully higher over the next 12 to 18 months as the full impact of enterprise AI adoption flows through income statements. Recent analysis from Bloomberg has echoed similar themes, noting that investor sentiment is increasingly shifting toward software names with clear AI monetization strategies.
What the $2 Trillion Figure Actually Means for Investors
When Ives talks about a $2 trillion AI software spending opportunity, he is not suggesting that this will materialize overnight. Rather, he is describing a multi-year spending cycle that will unfold over the remainder of this decade. The implication for investors is that the current valuations of many AI-exposed software companies — while elevated by historical standards — may actually be reasonable when measured against the total addressable market that is emerging.
This is a critical point because valuation concerns have been the primary objection raised by bears. Many AI software stocks trade at 15 to 30 times forward revenue, levels that would be difficult to justify in a normal spending environment. But if the total addressable market is genuinely expanding at the rate Ives projects, then current revenue bases represent only a small fraction of where these companies could be in five years. The math changes dramatically when you model sustained 25% to 40% annual growth rates over a multi-year period.
The Risks That Could Derail the Thesis
No investment thesis is without risk, and the AI software bull case has several potential vulnerabilities. A significant economic downturn could cause enterprises to pull back on technology spending, regardless of the long-term strategic value of AI tools. Regulatory action — particularly in the European Union, where AI governance frameworks are being actively developed — could slow adoption or increase compliance costs. And there is always the possibility that the technology itself disappoints, that AI models plateau in capability before they reach the level of reliability and accuracy needed for mission-critical enterprise applications.
Ives has acknowledged these risks but has argued that the probability-weighted outcome still favors the bulls. He has pointed to the breadth of enterprise adoption, the scale of infrastructure investment already committed, and the competitive dynamics forcing companies to adopt AI or risk falling behind. In his view, the question is not whether AI software spending will accelerate — it is which companies will capture the largest share of that spending. For investors willing to take a multi-year view, he believes the current moment represents an attractive entry point into what he has called the most significant technology spending cycle since the advent of cloud computing. The coming quarters will reveal whether his conviction is justified by the numbers.