Artificial intelligence is still one of the most powerful long-term investment themes in the market, but not every company with AI in its story is positioned to win. Some firms are still in the early stages of proving product-market fit, scaling revenue, or generating consistent cash flow. Others already sit at the center of the AI buildout, selling the picks and shovels that power real-world deployment.
That’s why many investors may want to skip BigBear.ai and focus instead on companies with stronger competitive advantages, clearer monetization, and deeper integration into enterprise AI budgets. Below are two AI stocks that look better positioned right now based on their role in the AI ecosystem and their ability to convert demand into durable revenue.
Why Some Investors Are Cautious on BigBear.ai
BigBear.ai operates in the AI analytics and decision intelligence space, often tied to government, defense, and complex enterprise workflows. That can sound compelling, but it also comes with challenges that can make the stock higher risk for many portfolios.
Key concerns to consider
- Lumpier revenue profiles: Contract-driven businesses can be dependent on timing, renewals, and large deal cycles.
- Execution risk: Turning AI capabilities into repeatable, scalable products is hard, especially amid fast-changing competition.
- Profitability and cash flow questions: Many smaller AI firms face pressure to fund growth while proving margins can expand over time.
This doesn’t mean the company cannot succeed. But if your goal is to own AI exposure with more predictable demand drivers and a stronger moat, it may make sense to look elsewhere.
Better AI Stock #1: Nvidia (NVDA)
If you want direct exposure to the AI boom, Nvidia remains one of the most important companies to watch. Its graphics processing units (GPUs) are foundational hardware for training and running advanced AI models, and the company has built a broader platform that spans networking, software, and developer tools.
Why Nvidia remains a core AI leader
Nvidia’s advantage isn’t just that it sells powerful chips. It has built an ecosystem that can be difficult for customers to replace quickly, particularly at scale. That matters because AI adoption is moving from experimentation to deployment, where reliability, performance, and tooling can be just as important as raw compute.
- Dominant position in AI compute: Many of the world’s leading AI workloads are built and optimized around Nvidia hardware.
- Software and ecosystem lock-in: Tooling and libraries (such as CUDA and a rich developer ecosystem) can significantly increase switching costs.
- Expanding beyond chips: Nvidia also benefits from networking demand and full-stack data center buildouts needed to support AI clusters.
What makes Nvidia better than smaller AI pure-plays
In many cases, Nvidia benefits regardless of which AI applications win, because it powers the infrastructure layer. Whether the demand comes from cloud giants, enterprise data centers, or government labs, the common denominator is compute. That makes Nvidia more like a platform investment tied to the AI buildout than a single-application bet.
Risks to keep in mind
- Valuation sensitivity: High expectations can amplify volatility if growth slows.
- Cyclical dynamics: Semiconductor demand can be cyclical, even in long-term growth markets.
- Competition: Rivals and in-house chips from large tech firms can pressure pricing over time.
Still, for many investors, Nvidia represents a higher-quality way to own AI than smaller, less-proven names because it sits at the center of AI infrastructure spend.
Better AI Stock #2: Microsoft (MSFT)
Microsoft may not look like an “AI stock” at first glance, but it’s arguably one of the most strategically positioned companies in the entire AI economy. The reason is simple: Microsoft owns one of the most powerful distribution engines in tech through Windows, Microsoft 365, GitHub, and enterprise relationships, while also operating a major cloud platform with Azure.
Microsoft’s AI edge: distribution plus cloud
AI is not just a research story anymore. It’s increasingly a workflow story, meaning the winners will be companies that can embed AI into tools users already rely on every day. Microsoft is doing exactly that by weaving AI features into products enterprises budget for annually.
- Azure as an AI engine: AI workloads drive cloud consumption, and Azure is a major beneficiary of that trend.
- Enterprise software integration: AI features inside Microsoft 365 can boost retention and open new pricing tiers.
- Developer ecosystem: GitHub and developer tools provide a channel to monetize AI-assisted coding and productivity.
Why Microsoft can be a “safer” AI bet
Compared with smaller AI companies, Microsoft offers more balanced exposure. You’re not relying on one product line to succeed. Instead, you get a diversified business with AI upside layered onto an already massive cash-generating core.
That diversification can matter if AI spending goes through pauses, shifts from training to inference, or becomes more cost-conscious over time. Microsoft can adapt because it monetizes AI across cloud, productivity software, security, and developer tools.
Risks to keep in mind
- Competitive cloud market: Azure competes with other hyperscalers, and price or feature competition can be intense.
- Execution and rollout: Embedding AI into enterprise workflows requires trust, reliability, and governance features.
- Regulatory scrutiny: Large platforms face ongoing regulatory and antitrust attention.
Even with those risks, Microsoft is often viewed as one of the strongest examples of how to operationalize AI at scale and turn it into recurring revenue.
How to Choose the Right AI Stocks (Beyond the Hype)
If you’re evaluating AI investments, the biggest mistake is assuming every AI company will benefit equally. In reality, the market is likely to reward firms that can prove monetization, protect margins, and sustain demand even as AI becomes more competitive.
What to look for in AI stock candidates
- Clear unit economics: Evidence the company can scale revenue without scaling costs at the same rate.
- Durable advantage: Hardware ecosystems, distribution, proprietary data, or platform stickiness.
- Recurring revenue: Subscription and usage-based models tend to hold up better than one-off project work.
- Real customer budgets: Products tied to cloud spending, productivity, or mission-critical operations.
By those standards, Nvidia and Microsoft stand out because they are directly tied to the largest and most sustained categories of AI spending: compute infrastructure and enterprise deployment.
The Bottom Line: Consider Skipping BigBear.ai for Higher-Quality AI Exposure
BigBear.ai may appeal to investors who want a smaller, more speculative AI play with government and enterprise angles. But for many portfolios, the risk-reward may look less attractive compared with AI leaders that already command ecosystem power and consistent demand drivers.
If you’re looking for two AI stocks to buy now with stronger competitive positioning, consider:
- Nvidia (NVDA): A core infrastructure provider powering AI training and deployment across the data center world.
- Microsoft (MSFT): A software and cloud giant embedding AI into enterprise workflows while monetizing demand through Azure.
AI is a long-term trend, but stock selection still matters. Focusing on companies with proven scale, strong moats, and clear monetization can be a more reliable way to participate in the AI boom than betting on smaller names that still need to prove consistent execution.
Disclaimer: This article is for informational purposes only and does not constitute investment advice. Consider your risk tolerance and consult a qualified financial professional before making investment decisions.