2 AI Stocks Wall Street Says Could Soar 70% or More From Here, and 1 It Says to Sell Immediately

Nebius stands out with nearly $50 billion in contracted revenue against just $530 million in 2025 sales, while analysts rate it a buy with targets of $143 to $211 after Nvidia's $2 billion strategic investment and Meta's up-to-$27 billion deal. Linde is highlighted as an AI-adjacent beneficiary of a helium supply shock, with JPMorgan upgrading it to Overweight and a $525 target due to tight helium markets. C3.ai is the negative counterpoint: shares are down more than 55% year to date, revenue fell 46% year over year, guidance was cut by about $51 million, and consensus has shifted to Moderate Sell.

Analysis

The market is splitting AI into two very different businesses: scarce infrastructure with contracted visibility versus software narratives that still need proof. Nebius is not just a growth story; it is effectively a financing-through-contracts event, where hyperscalers are pre-committing capacity before the company has built the revenue base. That creates a multi-year earnings runway, but the second-order effect is that supplier concentration and execution risk remain high: any delay in data-center buildout, power delivery, or GPU allocation can turn a backlog story into a working-capital trap.

Linde’s setup is more subtle and, in some ways, cleaner. A helium shock is a classic volume-negative, price-positive event for an incumbent with storage depth, so the margin impulse should arrive before end-demand meaningfully adjusts. The real implication is that chip fabs will likely pay up rather than halt production, which means industrial gas becomes an embedded tax on the AI supply chain; that supports Linde, but also raises the cost floor for AI hardware ramp and could modestly compress downstream AI server economics over the next few quarters.

C3.ai is the mirror image: the market is no longer paying for optionality because the operating data is deteriorating faster than the AI label can offset it. The key risk is not just further downside in the equity, but that strategic alternatives become a dilutive overhang if the company has to sell from weakness. In contrast, the consensus may be underestimating how quickly public-market AI capital is migrating away from application-layer names with weak retention toward infrastructure names with contracted demand and strategic scarcity.

The contrarian read is that Nebius may already be partly commoditizing into a capacity utility: great for backlog, but not automatically great for long-term ROIC if hyperscalers and strategic partners eventually demand lower margins. For Linde, the upside may be less about a one-time spike and more about a durable repricing of helium as a geopolitical strategic input, which could reset contract terms across the industry for 12-24 months.

Key Decisions for Investors

• Long NBIS on a 3-6 month horizon, but size it like a buildout/execution trade rather than a pure growth multiple story; best entry on pullbacks toward support after contract-related hype fades. Risk/reward favors the long side as long as backlog conversion milestones remain intact.
• Pair trade: long LIN / short AI for the next 1-2 quarters. This expresses preference for hard-asset scarcity with pricing power over software with deteriorating fundamentals; upside is driven by margin expansion on LIN and continued multiple compression on AI.
• Avoid chasing NBIS at peak enthusiasm; instead, use call spreads or staggered entries to reduce downside if infrastructure timing slips. The key risk is operational delay, not demand disappearance.
• Short AI outright or via put spreads into any relief rally over the next 30-60 days. The thesis is that sell-side downgrades typically lag the fundamental reset; downside can continue as revisions and guidance cuts feed each other.
• For defensive exposure to AI supply chain inflation, consider a tactical long LIN versus a basket of semiconductor equipment or AI-infrastructure beneficiaries with lower pricing power. This hedges the risk that input-cost shocks slow the broader AI buildout while preserving exposure to the strongest monopolistic bottleneck.