Goldman Sachs has upgraded enterprise software company Palantir Technologies Inc. (PLTR.US) from Neutral to Buy, citing the stock's recent underperformance and improved valuation appeal.

Goldman Sachs analyst Gabriela Borges wrote in a note to clients: "We are upgrading Palantir Technologies Inc. from Neutral to Buy with a 12-month price target of $230, implying approximately 18% upside." She noted that at Palantir Technologies Inc.'s current revenue run-rate of roughly $8 billion and growth rate of about 100%, investors are primarily debating two questions: first, whether the best part of the company's AI opportunity has already been realized or whether the market is deep enough to support another round of upward estimate revisions; and second, how sustainable the company's FDE model is.

Borges stated that the main takeaway from a recent series of industry conversations is that the stock is positioning itself for another phase of outperformance through 2027. She also believes that Palantir Technologies Inc.'s total addressable market (TAM) may be undergoing "yet another step-change," partly driven by sovereign AI, custom applications, and the company's new verticalization strategy.

Additionally, Borges explained Palantir Technologies Inc.'s FDE model. FDE typically refers to Forward Deployed Engineer, a model that requires a tight feedback loop between field teams and product teams to quickly understand customer needs and deploy solutions. She believes Palantir Technologies Inc. has refined this model to be "nearly perfect, and can even be automated through AI FDE."

Market observers note that Goldman Sachs' upgrade comes at a time when Palantir Technologies Inc. has recently lagged and investors are divided over the high valuations and growth sustainability of AI-concept stocks. Goldman Sachs' optimistic view implies that it believes enterprise AI demand, government contracts, and vertical industry expansion could still drive Palantir Technologies Inc. into a new growth cycle.

However, the report also implies two key risks: whether the AI opportunity has already been fully priced in, and whether the FDE model can continue to maintain efficiency as it scales.