Artificial intelligence has taken center stage in technology investing. Big names like NVIDIA, Microsoft, Google, Apple, Amazon, and Tesla dominate headlines—but the real opportunity might lie in the shadows. The most explosive moves often come from under-the-radar AI stocks: companies with small market caps, fast-growing revenues, and a clear product fit for the AI-driven world ahead.
Check out my complete AI stock watchlist here!
These companies aren’t household names yet. But they’re powering AI infrastructure, data preparation, smart hardware, and automation platforms behind the scenes. If you’re looking for AI exposure outside of the crowded mega-cap field, these seven names should be on your watchlist.
Table of Contents
7 Under-the-Radar AI Stocks to Watch
| Company | Ticker | Primary AI Focus |
| Arista Networks | NYSE: ANET | Networking for AI data centers |
| Vertiv Holdings | NYSE: VRT | Power and cooling for AI infrastructure |
| Innodata | NASDAQ: INOD | Data engineering for AI models |
| Ambarella | NASDAQ: AMBA | Edge AI chips for vision-based systems |
| Symbotic | NASDAQ: SYM | Warehouse robotics with AI integration |
| nVent Electric | NYSE: NVT | Electrical systems for AI hardware |
| GE HealthCare | NASDAQ: GEHC | AI in medical imaging and diagnostics |
Before you send in your orders, take note: I have NO plans to trade these stocks unless they fit my preferred setups. This is only a watchlist.
The best traders watch more than they trade. That’s what I’m trying to model here. Pay attention to the work that goes in, not the picks that come out.
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Arista Networks (NYSE: ANET)
Arista Networks isn’t flashy, but it’s foundational. This $119 billion networking company builds the Ethernet switches that power hyperscale AI data centers. It’s become a favorite among names like Microsoft and Meta, who together drive over one-third of Arista’s revenue.
The stock’s had a volatile ride—falling nearly 50% earlier this year before bouncing back strong. Traders blamed tariff uncertainty and competition fears, but the company’s Q1 earnings proved it’s still in control. Revenue rose 27.6% year over year to $2 billion. Product revenue alone hit $1.69 billion, thanks to surging AI investments from cloud platforms.
Having watched this stock since its early public days, I’ve seen Arista repeatedly shake off skepticism. Management has beaten estimates for 20 straight quarters. That kind of execution is rare in tech—especially with AI infrastructure still in early innings.
If you’re looking for a picks-and-shovels play tied to NVIDIA’s GPU growth and Microsoft’s cloud buildout, Arista deserves a spot on your screen.
Vertiv Holdings (NYSE: VRT)
If you want to trade the AI data center boom without chasing semiconductors, look at Vertiv. This under-the-radar play provides the thermal and power infrastructure that keeps AI hardware running at full speed.
Vertiv’s systems are embedded in the most advanced AI environments, including a new reference design built for NVIDIA’s GB300 platform. With demand rising for liquid cooling and high-density power racks, Vertiv is riding the wave. Q1 revenue jumped 24% year over year to $2.04 billion. Earnings per share surged 49%.
I’ve tracked Vertiv through various cycles, and their operational leverage is finally kicking in. They’ve got a $7.9 billion backlog and are gaining traction with hyperscalers. As traders, we want names aligned with secular trends that also have improving margins and order books. Vertiv checks all three boxes—and it’s not even on most investors’ radar.
Innodata (NASDAQ: INOD)
Innodata is a pure-play on one of the most critical inputs to artificial intelligence: labeled data. Without clean, annotated datasets, even the best AI models underperform. That’s where Innodata shines.
With over 6,000 in-house experts across the globe, Innodata delivers structured, secure data services to some of the largest tech companies in the world. Q1 2025 revenue surged 120% year over year to $58.3 million. Net income came in near $8 million—its first major step toward sustained profitability.
From a trader’s lens, this is a momentum story with a huge market. AI spending by firms like Microsoft and Google is climbing fast. Innodata’s ability to land Big Tech clients and scale their services shows strong product-market fit. I’ve seen plenty of AI startups crash under operational pressure, but Innodata’s 35+ years in data engineering makes it one of the few small-caps that’s built to last.
Ambarella (NASDAQ: AMBA)
Ambarella develops edge AI processors that power vision-based devices like smart cameras, drones, vehicles, and IoT hardware. While it’s not new to tech traders, it’s only recently re-emerged as a serious AI contender.
After a tough 2024, Ambarella has come roaring back. Q1 FY2026 revenue grew 57.6% to $85.9 million, marking its fourth straight quarter of record AI sales. Non-GAAP net income turned positive. 70% of its top line now comes from edge AI processors, which are key to real-time applications outside traditional data centers.
Having traded chip stocks through multiple hype cycles, I’m always wary of inflated valuations. But Ambarella’s price-to-sales ratio has pulled back to around 9, and they just raised full-year revenue guidance. Analysts see 55% upside. For traders hunting high-growth AI hardware names that aren’t NVDA, AMBA offers an attractive setup.
Symbotic (NASDAQ: SYM)
Symbotic uses AI and robotics to automate warehouses for retail giants like Walmart and Albertsons. It’s not a chip company or software name—but it’s building the brains behind physical AI systems that move products efficiently through supply chains.
Q2 revenue hit $550 million, up 40% from the prior year. Adjusted EBITDA more than tripled. What really stands out is the company’s $22.4 billion backlog—an insane number that provides long-term revenue visibility.
I’ve always taught traders to follow institutional money and customer validation. Walmart not only uses Symbotic but sold them their own robotics business. That’s a rare kind of endorsement. As AI pushes into logistics and physical automation, Symbotic’s platform is perfectly positioned. It’s not yet profitable under GAAP, but that’s typical for infrastructure builders in the scaling phase. The risk is there—but so is massive upside.
nVent Electric (NYSE: NVT)
nVent may not sound like an AI stock, but its electrical solutions are becoming mission-critical in AI data centers. From heat management to liquid cooling systems, its products support high-performance computing environments where NVIDIA chips run hot.
Sales in its Data Solutions segment are rising double digits. The company just completed a $975 million acquisition that expands its industrial presence. It also launched new liquid cooling products for NVIDIA’s next-gen GPU platforms, strengthening its ties to the AI hardware supply chain.
What caught my eye was its steady revenue growth and strong return on equity. Even with just a 6.7% ROE, nVent grew net income 31% over five years—impressive given its lower profile. As more capital flows into AI data center infrastructure, nVent’s steady performance and product innovation should give it a reliable spot in a growth-oriented portfolio.
GE HealthCare Technologies Inc. (NASDAQ: GEHC)
AI in healthcare is no longer a theory—it’s driving real change. GE HealthCare is making that transition tangible, integrating AI into imaging systems, diagnostics, and clinical workflows.
It’s easy to overlook GEHC as an AI stock. But its new solutions like MIM Encore and Invenia 3D Ultrasound are powered by AI algorithms that improve scan speed, diagnostic accuracy, and patient outcomes. GEHC is also working with NVIDIA to develop autonomous medical imaging solutions, tapping into the GPU power behind most advanced AI platforms.
I’ve always said that practical AI applications will win in the long run. GEHC isn’t about hype—it’s about execution. It just posted a 10.9% earnings surprise and is expanding its margin profile even in a tough regulatory environment. With the molecular imaging market expected to grow steadily, this is a long-term compounder for traders who want lower volatility AI exposure.
Under-the-Radar AI Stocks vs Top AI Stocks
When comparing under-the-radar AI stocks to well-known names like NVIDIA, Microsoft, or Amazon, the trade-offs are clear. Top AI stocks offer predictability, established business models, and deep analyst coverage. They’re often less volatile and serve as the backbone of most tech ETFs and S&P 500 portfolios.
Under-the-radar names are different. They typically have smaller market caps, limited institutional coverage, and higher volatility. But that also means greater potential for outsized returns. A well-timed trade on a small-cap AI stock that secures a new customer or hits earnings can double in weeks. You rarely get that with a $2 trillion company.
As a trader and mentor, I always suggest diversifying your exposure. Blend large-cap AI leaders with emerging tech stocks. Use your trading platform’s alerts to monitor price action, research, and earnings. Balance liquidity with opportunity. It’s not about finding the next NVDA—it’s about positioning for asymmetric upside.
Challenges and Considerations when Buying Small-Cap AI Stocks
Limited Analyst Coverage: Smaller AI companies often go unnoticed by Wall Street analysts. That means fewer earnings estimates, less institutional research, and fewer analyst upgrades to spark momentum. As traders, we have to do our own research and act early.
Liquidity Risks and Low Trading Volumes: Low volume can kill a trade. Wider bid-ask spreads make it harder to get good fills. If a stock gets hot, it might gap without giving you a clean entry. If it sells off, you might get stuck. Always watch the VIX and volume.
Regulatory and Technological Uncertainty: Emerging AI stocks face risk from changing rules—especially if outsourcing, privacy laws, or AI model regulation tighten. They’re also vulnerable to tech shifts. If NVIDIA releases new chips or Apple changes iOS architecture, some suppliers can get blindsided.
Competitive Pressure from Major AI Firms: Big Tech—Google, Amazon, Meta, and Microsoft—can replicate features or enter new verticals overnight. If a startup relies too heavily on one platform or funding source, that’s a red flag. Diversified revenue and long-term contracts help.
Is Now a Good Time to Buy Under-the-Radar AI Stocks?
With AI capital expenditures from the top five U.S. tech firms expected to top $200 billion this year, the market is growing fast. While mega-cap stocks are pricing in a lot of future growth, small and mid-cap names are still catching up.
That means there’s still opportunity—if you know where to look and how to manage risk. Use proper position sizing. Don’t overexpose your portfolio to illiquid names. Set alerts around earnings, guidance, and news. Watch for volume breakouts. Momentum can come fast.
We’re also at a phase in the market cycle where innovation is being rewarded again. The VIX is off its highs. Inflation is cooling. If rate cuts come later this year, small-cap growth stocks should benefit. That’s a tailwind for under-the-radar AI companies.
Key Takeaways
- AI infrastructure is more than just chips. Networking, power, data, and automation all present tradable opportunities.
- Under-the-radar stocks are volatile but offer strong upside. Think asymmetric risk. Manage your exposure.
- Arista, Vertiv, and Innodata are leading small-cap AI plays worth watching for revenue acceleration and analyst upgrades.
- Blend established and emerging names in your AI portfolio to spread risk and maximize opportunity.
This is a market tailor-made for traders who are prepared. AI stocks thrive on volatility, but it’s up to you to capitalize on it. Stick to your plan, manage your risk, and don’t let FOMO drive your decisions.
These opportunities are fast and unpredictable, but with the right strategy, you can make them work for you.
If you want to know what I’m looking for — check out my free webinar here!
Frequently Asked Questions
Where Can I Buy Under-the-Radar AI Stocks?
You can buy under-the-radar AI stocks on major U.S. exchanges like the Nasdaq and NYSE through most brokerage platforms. Make sure your platform supports real-time analytics, clean execution on orders, and offers access to research tools like analysis reports, stock advisor insights, and newsletters. These are especially useful when trading less-followed names.
Look for brokers that let you place limit orders on lower-volume equities to avoid getting caught in wide bid-ask spreads. If you’re trading with smaller funds, start with manageable shares and consider adding exposure through options once liquidity improves. Use platform features to monitor stock price action, institutional funding rounds, and any IPO or pre-IPO developments if you’re seeking early entry into AI-related plays.
Are Small-Cap AI Stocks More Risky than Well-Known Ones?
Yes, small-cap AI stocks come with higher volatility, lower liquidity, and less visibility in broader markets. Many of them are in early funding rounds or still years from a potential IPO, so swings in stock price can be sharp and unexpected.
Big-name players in artificial intelligence—those with access to GPUs, proven analytics platforms, and deep enterprise contracts—often provide more stable performance. But if you’re a trader willing to manage trades with tight risk controls and well-placed orders, you can capture the growth potential these emerging names offer.
Always cross-check with analysis, reviews, and institutional research data. These sources can alert you to red flags—or help you spot mispriced opportunities before the market catches on.
Which Under-the-Radar AI Stocks Have Strong Fundamentals?
Strong fundamentals in small-cap AI stocks typically mean healthy revenue growth, expanding customer bases, positive cash flow, and improving gross margins. Based on recent analysis reports and earnings reviews, companies like Innodata, Symbotic, and Vertiv Holdings stand out. They’re showing traction with enterprise-level clients and positive signals in funds flow and order backlog.
Traders should monitor stock price trends alongside analytics around earnings per share (EPS), order momentum, and capital allocation. Look at their use of GPUs, customer stickiness, and whether they’re entering or benefiting from active funding rounds. This is often where fundamental tailwinds turn into actual trades with upside.
Use your platform’s equities screener, then verify insights using a reliable stock advisor service or institutional-grade research data.
Should I Expect Profitability from “Under the Radar” AI Companies?
Not necessarily—at least not immediately. Many of these names are in expansion mode, reinvesting every dollar from funding rounds into R&D, product scaling, or client acquisition. Profitability is often a secondary goal until key AI models are trained or enterprise deployments go live.
However, look for improving margins, reduced operating losses, and guidance on break-even timelines. Companies like Ambarella and GE HealthCare have shown clear signs of operating leverage, while Innodata has recently swung to profitability on the back of major AI contracts.
Traders should weigh stock price movement against burn rate and growth runway. Use analytics, reviews, and newsletter insights to measure whether the business model supports eventual profitability. Pay close attention to their use of GPUs, customer dependency, and potential for recurring trades in platform usage.
Look for transparency in analysis reports and investor updates—these will tell you if the company is built for a long runway or just riding hype.