Palantir reported first-quarter results that exceeded analysts’ expectations and lifted its outlook, fueling optimism around the company’s ability to monetize surging demand for AI-enabled software across government and commercial customers.

The company reported adjusted earnings of 33 cents per share on revenue of $1.63 billion, topping LSEG consensus estimates of 28 cents and $1.54 billion, respectively. Palantir said revenue rose roughly 85% from a year earlier — its fastest growth rate since at least 2020.

Profitability also improved sharply. Net income roughly quadrupled to $870.5 million, or 34 cents per share, from $214 million, or 8 cents per share, a year earlier.

Palantir raised full-year guidance, forecasting adjusted free cash flow of $4.2 billion to $4.4 billion, above the StreetAccount consensus. Management also guided to about $1.8 billion in second-quarter revenue, ahead of expectations.

For 2026, Palantir now expects revenue of $7.65 billion to $7.66 billion, representing about 71% annual growth and above consensus forecasts. CEO Alex Karp argued in a shareholder letter that the company’s results demonstrate exceptional strength at scale, highlighting revenue per employee of $1.5 million on an annual basis.

The quarter underscored Palantir’s deep ties to U.S. defense and security priorities. Revenue from domestic government agencies climbed 84% to $687 million, accelerating from the prior quarter. U.S. commercial revenue jumped 133% to $595 million, though it came in slightly below estimates.

Palantir also reported expansion in its commercial customer base and a large increase in remaining performance obligations — a forward-looking indicator of contracted but unrecognized revenue.

Investors will continue to watch whether Palantir can sustain elevated growth while navigating a competitive AI landscape and broader software-sector volatility. For now, the quarter reinforced the market’s view that demand for operational AI deployments — not just foundational models — is translating into revenue at meaningful scale.