Palantir (PLTR.US) earnings are expected to beat expectations again! Morgan Stanley remains Neutral, saying a pullback is the right opportunity to build a position.

Palantir (PLTR.US) will report earnings next week. Morgan Stanley released a preview report, stating that based on channel and partner interviews, the company is expected to deliver another beat-and-raise quarter, achieving its thirteenth consecutive quarter of accelerating growth. However, faced with a high valuation that has already fully reflected optimistic expectations, the analyst maintains an Equal-weight rating, cautioning that the market needs to take a rational view of the AI-driven boom and that a pullback in the share price would be a better entry window. Judging from industry research feedback, demand for enterprise data modernization and AI operational decision-making projects remains robust. Leveraging its Ontology layer and AIP artificial intelligence platform, Palantir can integrate heterogeneous data within enterprises and implement business-oriented AI decision-making processes, creating differentiated competition versus data warehouse players such as Snowflake and general-purpose large-model vendors, with limited direct competitive pressure. However, shortcomings in the business model are equally prominent: upfront implementation costs for projects are relatively high, with heavy reliance on forward-deployed engineers, which has continuously fueled market debate over whether it is "software or consulting"; meanwhile, the financial services vertical has yet to achieve breakthrough progress, and evidence of accelerating new customer expansion is not sufficiently strong. On the performance front, the company's official Q3 revenue guidance is US$2.16-2.164 billion, up 83% year over year. Morgan Stanley expects a 5-6% revenue beat this quarter, lower than the 7.6% significant beat in Q2, with actual revenue potentially reaching US$2.27-2.29 billion, raising year-over-year growth to 92-94%. The US commercial segment remains the core growth engine, with Q2 revenue surging 149% year over year, but a higher base combined with customer count growth slowing from 42% to 35% makes further acceleration more difficult; sustaining growth of around 140% would already be a favorable outcome. Q4 guidance will become the most important indicator to watch going forward and will also determine whether the full-year revenue guidance is raised. Financially, the company maintains extremely strong expense control, with operating margin stable at a high level of nearly 60%. Commercial business growth is significantly higher than government business, while government orders are steady but growth is gradually moderating. Revenue is highly concentrated geographically in the North American market, with all other regions contributing relatively low shares. The biggest contradiction in the report comes from valuation. The current share price corresponds to about 70 times 2027 free cash flow, almost fully pricing in optimistic assumptions about AI commercialization, leaving very little margin for error. Even if fundamentals continue to boom, the base-case target price of US$205 offers limited upside from the current price of US$196.81. Based on DCF cash flow discounting, the bull-case target price is US$295, with an option-implied probability of realization of only 9.3%; under the bear case, the target price drops to US$81. Overall, Palantir's product differentiation and enterprise AI demand are real, and the Q3 report has the potential to beat expectations, but the high valuation has already priced in most of the positives. Future core upside catalysts come from faster acquisition of new commercial customers and government budget orders exceeding expectations; main risks lie in performance volatility from large orders, controversy over the delivery model, and AI commercialization falling short of expectations. For investors, tactically acknowledging the quarterly earnings elasticity is warranted, but chasing highs is not advisable; it is better to wait for a share price pullback before reassessing allocation value.