Shares of Palantir Technologies climbed toward a nearly one-year high on Thursday as Goldman Sachs analyst Gabriela Borges reversed her long-standing skepticism and recommended investors buy the stock. The upgrade highlights a shift in outlook regarding the company’s growth trajectory amid the expanding artificial intelligence sector.
Borges, who had maintained a neutral rating on Palantir for at least three years, indicated that the total addressable market is poised for significant expansion. She pointed to the rising demand for “sovereign AI,” a trend where governments and enterprises seek to build and deploy AI systems without relying on external cloud providers or model vendors.
The stock rose 2.4% during midday trading, positioning it for its highest close since November 3, 2025. This performance stood out against a broader technology selloff, with the Nasdaq 100 dropping 0.7%. The rally comes despite Palantir having significantly outperformed the market in previous years, with shares surging 340% in 2024 and 135% in 2025.
In contrast to those explosive gains, Palantir has gained a more modest 12.2% year-to-date in 2026, lagging behind the Nasdaq 100’s 22.7% advance. Borges argued that this relative underperformance, combined with growing market opportunities, has created a chance to acquire shares at a discounted valuation. She set a price target of $230, implying approximately 15% upside from current levels.
Borges also addressed potential concerns about Palantir’s business model, which relies heavily on forward-deployed engineers working on-site with clients. While some investors question the scalability of this approach, Borges noted that it enables real-time feedback and rapid product improvements, helping the company maintain a competitive edge over other software providers.
Additionally, she highlighted the company’s pivot toward a more vertical model, adapting its platforms to specific industries. Borges emphasized the importance of Palantir’s Ontology product, which creates digital twins of enterprise data to map real-world objects and workflows. As AI applications evolve into agentic systems capable of autonomous operation across multiple tasks, she described the operational layer as increasingly critical.
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