Wall Street's Top AI Growth Picks: Nvidia and Applied Digital Lead Analyst Favorites

After a first-quarter correction in the Nasdaq Composite, investors began piling back into technology and artificial intelligence stocks in April 2026. The pullback wasn't driven by revenue slowdowns or demand issues — stocks were simply trading at unusually high multiples, with some AI names far above expected earnings. Now, with valuations compressed, Wall Street analysts are pointing to two specific names as their top picks.

The coverage comes from The Motley Fool's April 23, 2026 analysis, which identifies Nvidia and Applied Digital as the stocks analysts are "in love with right now." The report notes that investors should still be concerned about high P/E ratios for AI stocks, because many remain overvalued and may not have the high demand or strength in their market to sustain the high prices.

Applied Digital (NASDAQ: APLD) sits at the heart of the AI boom as an owner and developer of AI data centers. The emergence of AI has created the need for AI factories — facilities that can handle the high-performance computing needs that AI applications demand. According to Motley Fool research, some $4 trillion is projected to be spent on data centers in 2030 worldwide, up from $1 trillion in 2025. That's a four-fold increase in five years (which is either incredibly optimistic or the kind of projection that makes CFOs nervous).

Applied Digital was one of the first movers in purpose-built AI data centers. Its first data center dedicated to AI processing is open in North Dakota, fully rented by cloud company CoreWeave. The company also has three legacy Bitcoin mining centers, but the focus of its operations is now on AI data centers. It has four new facilities in the works, including three at its Polaris Forge campus in North Dakota and one at Delta Forge in Texas. All four will be operational between late 2026 and the end of 2027.

Oracle has been signed to fill one of them while the company is in negotiations with another hyperscaler for its Texas facility. Interest is high among hyperscalers for all of these new facilities, CEO Wes Cummins said on the recent earnings call. Applied Digital is targeting $1 billion in net operating income (NOI) over the next five years. To put that into perspective, the company made $17.6 million in operating profit from its AI data centers and $13.9 million from its crypto mining facilities in its latest quarter — which is a total of $31.5 million. So massive growth is expected.

On Wall Street, 100% of the 13 analysts that cover it rate Applied Digital stock a buy, with a median price target of $43 per share. That would suggest 32% upside for the stock over the next 12 months or so. Investors should note that the company is not yet profitable and the forward P/E projection is high, but once these data centers start getting filled, the expectations are for high earnings. This is probably one you buy at a dip and wait for.

Nvidia (NASDAQ: NVDA) has been a Wall Street darling for years, and for good reason. It is the world's most valuable company and the dominant player in its industry, with nearly 90% market share in graphics processing units (GPUs) for AI data centers. While its stock price has stumbled in recent months, its revenue and earnings have not — in fact, they have accelerated. The pullback was mainly due to its high valuation and to investors likely cashing out after a three-year bull market run.

The sell-off created an incredible buying opportunity for the AI juggernaut, as its P/E ratio dropped to 41. But more importantly, its 12-month forward P/E ratio fell to 24, which is right around the Nasdaq average, while its five-year PEG ratio, based on long-term projected earnings, is below 1 at 0.72. That means it's a value. This is a great time to buy Nvidia stock, and analysts are mostly in agreement. Roughly 93% of the 70 analysts who cover Nvidia say it is a buy. It has a median price target of $267.50, which suggests about 33% upside over the next year.

Unlike Applied Digital, which is a more speculative play based on projections of filling its data centers, Nvidia is a no-brainer and a proven earnings machine that is not slowing down. The physical reality of Nvidia's dominance is visible in server rooms across the globe — rows of black GPU cards humming with heat, connected by copper and fiber, processing the workloads that power everything from chatbots to autonomous vehicle training.

Morningstar provides broader context with its own list of best AI stocks to buy, which includes Nvidia, Microsoft, Taiwan Semiconductor Manufacturing, Broadcom, Meta Platforms, Tencent Holdings, Oracle, Alibaba Group, Adobe, International Business Machines, Accenture, and Snowflake. Morningstar rates Nvidia as 4 stars with a wide economic moat, estimating the stock looks 22% undervalued relative to its $260 fair value estimate. Microsoft earns 5 stars and looks 30% undervalued relative to a $600 fair value estimate.

The Morningstar analysis notes that generative AI remains the largest theme within the sector. Software firms are developing and incorporating next-generation AI capabilities into their solutions, while cloud providers are introducing new services and scaling capacity, and some semiconductor firms like Nvidia are enjoying surging demand for AI and data center chips. The "anything-but-AI" sentiment in the market is leaving many of these stocks looking attractive after the Q1 selloff.

Investors should still be concerned about high P/E ratios for AI stocks, because many remain overvalued and may not have the high demand or strength in their market to sustain the high prices — and they may be more challenged by an economic or market downturn. Whether users actually pay for these AI capabilities remains the real question. The infrastructure is being built, the chips are being manufactured, and the data centers are being filled. But the revenue models for many AI applications are still being figured out, and that uncertainty is baked into every price target.

Artūras Malašauskas is an AI Systems Integrator with 20+ years of production-grade web engineering experience. He has designed, shipped, and scaled enterprise Python/PHP systems for logistics, SaaS, and public-sector clients. For the past year, he has focused exclusively on AI integrations: deploying open-source LLMs, building generative media pipelines (image, audio, video), and engineering multi-agent workflows for real production environments. His standard: reproducibility, security, cost-efficient inference—no vaporware. He documents and evaluates emerging AI tooling, separating verified capabilities from marketing noise. Technical editor at: muza-ai.eu, ai-verslas.lt, ai-naujinos.lt Connect on LinkedIn