Dan Ives argues AI valuations 2026 earnings potential remains underpriced. Discover why markets may be missing the biggest AI opportunity of the decade.
Here’s something that might keep you up at night if you’re an investor: What if the biggest AI stocks are actually undervalued? I know, I know — that sounds counterintuitive when we’re talking about companies with trillion-dollar market caps. But Dan Ives, one of Wall Street’s most influential tech analysts, is making exactly that argument about AI valuations 2026 earnings potential.
Despite a historic AI-driven rally that has minted new fortunes and transformed portfolios, Wedbush’s Managing Director argues that current AI valuations 2026 earnings projections remain fundamentally mispriced. The market, he suggests, is looking at today’s multiples while completely missing tomorrow’s earnings explosion.
Think about it this way: when everyone’s screaming “bubble,” maybe the real story is that we haven’t even seen the main act yet. According to Ives, only 3% of US companies have meaningfully deployed AI. Globally? Less than 1%. If that doesn’t make you reconsider how you’re thinking about AI valuations 2026 earnings trajectories, I’m not sure what will.
The central question for investors navigating this landscape is deceptively simple: Are markets systematically underestimating how quickly AI revenue transforms into earnings? And more importantly, what does this mean for how we assess AI valuations 2026 earnings across the entire technology sector?
Ives has been remarkably consistent in his thesis: current AI valuations 2026 earnings multiples reflect today’s revenue, not tomorrow’s operating leverage. Markets are essentially looking in the rearview mirror while driving forward at 100 miles per hour.
“We believe 2026 will be the year of AI monetization as the infrastructure leads to the use cases for enterprises and consumers,” Ives stated in a recent analysis. “This is just the beginning, and we expect a bullish 2026 for tech and the AI Revolution.”
The AI monetization lag is profoundly misunderstood. When you’re analyzing AI valuations 2026 earnings potential, you need to understand that enterprises don’t flip a switch and suddenly generate AI revenue. The deployment cycle takes 12 to 24 months — and we’re only now entering the sweet spot where early investments start paying dividends.
Ives projects that technology equities could climb more than 20% in 2026 as the second and third-order effects of AI adoption accelerate across industries. This isn’t wishful thinking — it’s math rooted in how AI valuations 2026 earnings dynamics work when enterprise adoption shifts from pilot programs to full production deployment.
Consider the trajectory: companies have spent the past two years building AI infrastructure, training models, and running pilots. Now, they’re moving from experimentation to execution. This transition is precisely why AI valuations 2026 earnings estimates need to be reassessed. Margins expand dramatically once infrastructure costs normalize and revenue starts flowing.
Not everyone shares Ives’ optimism on AI valuations 2026 earnings potential. The skeptics point to several legitimate concerns:
But here’s where the AI valuations 2026 earnings thesis gets interesting. Ives describes the current moment as a “1996 moment, not a 1999 bubble moment.” The difference matters enormously. In 1996, infrastructure was being built; in 1999, speculation ran wild. We’re still in the building phase.
Anyone analyzing AI valuations 2026 earnings needs to understand why the gap between AI spending and AI profits is so wide right now. The simple answer: you have to build before you can monetize.
Hyperscalers spent $106 billion in capex in Q3 2025 alone — a 75% year-over-year increase. GPUs, data centers, networking equipment, and talent all require massive upfront investment. But these costs don’t scale linearly with revenue. Once the infrastructure exists, each additional customer becomes increasingly profitable.
Enterprise AI adoption follows a predictable pattern: discovery, pilot, proof of concept, limited deployment, then full production. This cycle takes 12 to 24 months on average. When evaluating AI valuations 2026 earnings, you’re essentially betting on how many companies complete this journey within the next year.
The data here is encouraging. According to recent research, 78% of organizations now use AI in at least one business function — up from 55% just a year ago. The adoption curve is steepening precisely when AI valuations 2026 earnings models should start reflecting real-world monetization.
Initial gross margins in AI are compressed by infrastructure costs, but AI valuations 2026 earnings should reflect the long-term operating leverage that’s being built. Software businesses typically achieve 70-80% gross margins at scale. AI platforms are heading in the same direction — they just haven’t gotten there yet.
Metric | Data Point |
|---|---|
Big Tech AI Capex 2026 (Projected) | $550-600 Billion |
US Companies with AI Deployment | Only 3% |
Global AI Deployment Rate | Less than 1% |
Organizations Using AI (2025) | 78% (up from 55% in 2024) |
Gartner Global AI Spending Forecast 2026 | Over $2 Trillion |
Ives’ Tech Stock Growth Projection 2026 | 20%+ for Large-Cap Tech |
Not all AI valuations 2026 earnings trajectories are created equal. Understanding where the money flows helps identify where the returns concentrate.
The foundation layer captures value first. Chip makers, cloud platforms, and networking companies are seeing immediate revenue from the AI buildout. Nvidia’s forward P/E based on 2026 estimates sits below 25 times — remarkably reasonable for a company growing revenue 62% year-over-year. For AI valuations 2026 earnings analysis, infrastructure remains the most tangible play.
This is where AI valuations 2026 earnings potential gets truly exciting. Copilots, enterprise AI services, and vertical-specific AI applications are where the margin expansion story really plays out. Microsoft’s Azure AI, Palantir’s commercial expansion, and emerging AI-native SaaS companies all fit this category.
Consulting firms, system integrators, and managed AI operations providers represent the picks-and-shovels play for AI valuations 2026 earnings growth. Every enterprise deploying AI needs help implementing it. This creates a durable, recurring revenue stream that traditional valuation metrics often miss.
Traditional financial analysis struggles with AI valuations 2026 earnings projections for several structural reasons.
AI creates future revenue layers that don’t exist today. A company like Microsoft isn’t just selling cloud compute — it’s building a platform where every new AI capability creates new monetization opportunities. Standard AI valuations 2026 earnings models using trailing P/E completely miss this optionality.
Data flywheels and ecosystem lock-in create compounding advantages that linear earnings projections can’t capture. When analyzing AI valuations 2026 earnings, you need to account for how each new customer improves the product for every other customer.
AI earnings don’t grow steadily — they inflect suddenly when adoption reaches critical mass. This is precisely why Ives describes 2026 as the inflection year. Current AI valuations 2026 earnings estimates using linear extrapolation fundamentally misread how technology adoption curves work.
Remember when Amazon’s cloud business was seen as a money-losing distraction? Early cloud computing faced the same AI valuations 2026 earnings skepticism we see today. High capex, unclear ROI, questions about when profits would materialize. AWS now generates operating margins above 30%.
Apple’s App Store and Google Play monetization lagged smartphone adoption by years. The AI valuations 2026 earnings pattern follows this same trajectory — infrastructure first, then ecosystems, then profit explosion.
The critical difference? AI builds on existing infrastructure. Cloud, mobile, and internet connectivity already exist. Enterprise urgency is higher than any previous technology wave. This accelerates the AI valuations 2026 earnings timeline compared to historical precedents.
No investment thesis is complete without acknowledging what could go wrong. Here are the legitimate risks to the AI valuations 2026 earnings outlook:
If infrastructure spending continues accelerating without proportional revenue growth, margins could compress longer than expected. Some analysts worry that hyperscalers are consuming 94% of operating cash flow on AI infrastructure alone.
Both the US and EU are actively developing AI regulations. Depending on their scope, new rules could slow enterprise adoption and complicate AI valuations 2026 earnings projections.
If pilot-to-production conversion rates don’t improve from the current 5%, the AI valuations 2026 earnings thesis weakens considerably. Enterprises need to see clear ROI to justify continued spending.
As more players enter the AI market, pricing pressure could limit the margin expansion that drives attractive AI valuations 2026 earnings growth.
Here’s my take on why AI valuations 2026 earnings are being systematically underestimated: analysts are using product-company frameworks to value platform companies. When you sell a product, your addressable market is relatively fixed. When you build a platform, every new capability expands your market.
Software economics are brutal in the best possible way. Once you’ve built the product, the marginal cost of serving another customer approaches zero. AI valuations 2026 earnings need to reflect that AI software will eventually achieve these same economics — but faster because the distribution infrastructure already exists.
This is perhaps the most important insight for investors. AI valuations 2026 earnings will diverge dramatically. Some companies will prove the skeptics right — overvalued and underdelivering. Others will shock the market with earnings acceleration that makes today’s multiples look cheap in retrospect.
If you’re positioning around the AI valuations 2026 earnings thesis, here are the metrics that matter most:
These indicators will separate companies that justify their AI valuations 2026 earnings premiums from those that don’t.
Rank | Company | Ticker |
|---|---|---|
1 | Microsoft | MSFT |
2 | Nvidia | NVDA |
3 | Tesla | TSLA |
4 | Palantir | PLTR |
5 | AMD | AMD |
Dan Ives’ argument fundamentally reframes the AI valuation debate. While skeptics focus on current multiples and capex concerns, his thesis centers on a simple question: What happens when trillions of dollars in infrastructure spending finally converts into earnings?
The numbers tell a compelling story. With AI valuations 2026 earnings potential remaining underpriced according to Ives, and only 3% of US companies having meaningfully deployed AI, we’re nowhere near saturation. The market’s biggest mistake may be focusing on today’s multiples instead of tomorrow’s cash flows.
AI stocks may look expensive today. But if Ives is right about AI valuations 2026 earnings trajectories, they could look cheap in hindsight. The real AI re-rating may still be ahead — and investors who understand the earnings inflection thesis could be positioned to benefit.
The bottom line: The AI valuations 2026 earnings story isn’t about whether AI is transformative — it clearly is. It’s about timing. Ives is betting that 2026 represents the inflection point where infrastructure spending transforms into earnings power. History suggests he might be right.
What’s your take on AI valuations 2026 earnings? Are markets underestimating the opportunity, or is Ives too bullish? Drop your thoughts in the comments — I’d love to hear where you’re positioned heading into what could be the most important year for AI investing yet.
Disclaimer: This article is for informational purposes only and does not constitute investment advice. The views expressed are those of Dan Ives and other analysts cited. Always conduct your own research and consult with a financial advisor before making investment decisions.
Ives argues that current stock prices reflect today’s revenue and earnings, not the earnings explosion he expects when AI infrastructure investments start generating returns in 2026.
Enterprise AI adoption is shifting from pilots to production, infrastructure costs are normalizing, and monetization is accelerating — all of which should drive significant earnings growth in 2026.
Ives describes this as a “1996 moment, not a 1999 bubble moment,” suggesting we’re in the infrastructure-building phase rather than speculative excess. However, risks including regulatory changes and slower-than-expected enterprise adoption remain.
Infrastructure leaders (chips, cloud platforms), AI software and platform companies, and AI services and integration providers all present opportunities, with software potentially offering the highest margin expansion.
Big Tech is projected to spend $550-600 billion on AI capex in 2026 alone. While this initially compresses margins, it creates the infrastructure for future earnings growth as these investments start generating returns.
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