BigBear.ai lowered its full-year 2025 revenue guidance to $125 million–$140 million, citing disruptions on U.S. Army programs, while emphasizing a fortified balance sheet and a pipeline aligned with unprecedented federal AI spending.
Management withdrew adjusted EBITDA guidance to preserve flexibility for investment as Congress advances large-scale funding for homeland and defense technologies.
CEO Kevin McAleenan pointed to “landmark developments” including the One Big Beautiful Bill (OB3), which directs $170 billion to DHS and $150 billion to DoD for disruptive defense tech, framing it as a direct tailwind for the company’s mission AI footprint.
He highlighted biometric deployments across more than 2,000 devices at over 500 gates and 25 airports, alongside international expansion in the UAE and Panama. “We have the cash to invest, the conviction to advance,” he said, underscoring a strategy to go on offense despite near-term contract timing volatility.
Q2 2025 results reflected the Army program impact: revenue fell to $32.5 million, gross margin contracted to 25% from 27.8%, and adjusted EBITDA was negative $8.5 million versus negative $3.7 million a year ago. The company reported a net loss of $228.6 million, driven largely by a $136 million non-cash derivative remeasurement related to convertible notes and a $71 million goodwill impairment. SG&A declined to $21.5 million from $23.4 million as cost controls continued.
Liquidity is now a defining theme. Interim CFO Sean Ricker reported $391 million in cash and a net positive cash position of nearly $250 million, marking the first time cash exceeds total debt.
BigBear.ai raised approximately $293 million via at-the-market offerings, issuing roughly 75 million shares at an average price of $3.90, providing dry powder for organic growth, targeted M&A, and bid competitiveness as federal opportunities scale.
Management reaffirmed confidence in its midterm outlook, prioritizing pipeline expansion, contract recompetitions, and disciplined capital deployment to capture anticipated government AI demand, while acknowledging ongoing timing risks in federal awards.