2 Popular AI Stocks to Sell Before They Fall 53% and 58%, According to Wall Street Analysts

Jefferies assigns Palantir an $80 target, implying 53% downside from $172, despite Q2 revenue growth of 93% to $1.9B and non-GAAP EPS growth of 156% to $0.41. The key concern is valuation: Palantir trades at 68x sales, the highest multiple in the S&P 500, alongside concerns that it is underinvesting relative to faster-growing AI rival Anthropic. Morningstar's $62 SpaceX target implies 58% downside from $150, citing a 94x sales valuation and $25B of negative free cash flow in H1 2026 despite Q2 revenue rising 92% to $7.8B.

Analysis

The relevant risk is not simply valuation but a shift in what the market is underwriting: PLTR must sustain unusually high incremental growth while preserving software-like margins, leaving little tolerance for a deceleration in U.S. commercial bookings, federal contract timing, or net-revenue-retention. A premium multiple can persist through the next earnings print if guidance rises, but a single quarter of lower remaining-deal-value growth or materially higher sales-and-marketing spend would challenge the “efficient growth” premise and can drive multiple compression faster than EPS revisions. The more defensible relative short is PLTR versus a profitable software peer with a lower expectations burden, rather than an outright short into persistent retail momentum.

SPCX has a more asymmetric financing risk: large infrastructure programs turn operational milestones into capital-markets events. If free-cash-flow burn remains elevated, future equity issuance or secondary liquidity at a lower clearing price could reset private-market marks and pressure listed proxies with space/AI exposure; the first-order beneficiaries are likely launch, power, cooling, and data-center equipment suppliers only if contracted demand—not aspirational capacity—is confirmed. Claims around orbital compute should be valued as a long-dated option until unit economics, launch cadence, satellite replacement capex, and paying customer commitments are disclosed.

Contrarianly, bearish price targets alone are weak catalysts: both names have concentrated retail ownership and narrative momentum, so shorting immediately after a negative article risks a squeeze. The better catalyst window is 1-3 months around earnings, financing disclosures, and any indication that AI demand is converting into lower-margin services or capex rather than high-margin recurring revenue. Over 6-18 months, the key question is whether AI infrastructure returns normalize; if so, expensive application-layer and capital-intensive platform multiples should compress simultaneously, reducing the value of a simple long/short AI pair trade.

Key Decisions for Investors

• Do not initiate an outright PLTR short solely on analyst targets. Establish a watch trigger: short PLTR only after a guidance miss, a sequential slowdown in commercial bookings, or a break below the post-earnings low; target 20-30% downside over 3-6 months, with a hard stop on renewed raised guidance or a 15% move above entry.
• Preferred relative-value expression: long CRWD / short PLTR in equal dollar amounts over a 3-6 month horizon, sized modestly. The trade monetizes PLTR multiple compression while retaining AI/software exposure; exit if CRWD billings or retention deteriorates materially, or if PLTR demonstrates sustained acceleration without incremental operating-expense investment.
• For SPCX, avoid directional exposure until independently verified quarterly cash burn, committed AI-compute backlog, and financing plans are available. If a new financing round or disclosed cash-burn trajectory implies dilution, consider a tactical short only after liquidity terms are known; risk is a successful launch/technical milestone producing a sharp narrative repricing.
• Use GOOG as a watch-list beneficiary rather than a direct read-through long: confirmed third-party compute leasing can validate hyperscaler demand, but the investment case requires disclosure that external AI capacity demand is incremental rather than cannibalizing internal cloud utilization. Reassess following Cloud backlog and capex commentary.