#3 SCALE AI Private | Meta-Backed | Valuation: Reportedly approximately $29B
Founded in 2016 by Alexandr Wang, who dropped out of MIT at 19, Scale AI became a key player in the AI ecosystem by providing high-quality training data used to develop machine learning models, recruiting and managing contractors worldwide to label and quality-check the data that teaches AI systems how to think.
2024 Revenue: Reportedly approaching $1B | Government Contracts: Reportedly exceeding $300M in active DoD engagements
In mid-2025, Meta Platforms made a major strategic investment in Scale AI, reportedly acquiring a substantial non-voting stake and valuing the company at approximately $29 billion. Following the transaction, founder Wang transitioned to a role at Meta focused on AI strategy. Reports subsequently emerged suggesting that several major commercial customers reevaluated their relationships with Scale, citing concerns that may have included data governance and competitive considerations, though the motivations behind individual decisions have not been uniformly confirmed. The company also undertook a workforce reduction during this period, according to published reports.
The Palantir comparison is strategic rather than operational. Palantir operates at the decision layer. Scale AI operates at the training data layer, the foundational input that powers AI systems. As demand for high-quality, human-annotated data increases, this layer could become strategically critical. Scale’s involvement in U.S. defense-related AI programs, including reported DoD engagements valued above $300 million in aggregate, places it in adjacent competitive territory to Palantir’s government franchise.
Company representatives told CNBC in late 2025 that its data business grew on a monthly basis following the Meta transaction, and that its applications business showed meaningful acceleration in the second half of 2025 relative to the first half. In early 2026, Scale launched a new research division focused on agentic AI systems and robotics.
Bull Case: A structurally important position in the AI training data supply chain that is difficult to replicate. Government demand is increasing. The long-term scarcity of high-quality expert-annotated data may strengthen competitive advantages over time.
Key Risks: Reports of reduced engagement from several major commercial customers represent a meaningful revenue concentration risk. Leadership transition following Wang’s move to Meta introduces continuity questions. Regulatory bodies in certain jurisdictions have reportedly initiated reviews related to the Meta transaction, though outcomes remain uncertain. No IPO timeline has been announced.
Bottom Line: Scale AI represents a high-risk, high-upside position on the long-term importance of proprietary training data in AI. The events of 2025 introduced real uncertainty into a business that had previously shown exceptional commercial momentum. Public market investors may consider Meta (NASDAQ: META) as a vehicle for indirect exposure.
THE BOTTOM LINE
Palantir is a genuinely exceptional business. But at premium revenue multiples, it is pricing in a high degree of sustained execution over the next decade.
Databricks offers the most compelling large-scale pre-IPO infrastructure play. Glean represents a fast-growing bet on enterprise AI adoption at the workflow level. And Scale AI is a more complex but potentially critical player in the AI training data supply chain.
None is a direct substitute for Palantir, but together they reflect the broader question facing investors after Palantir’s breakout performance: is there a more efficient way to own the enterprise AI opportunity?
Disclosure: This article is for informational purposes only and does not constitute investment advice. Always conduct your own due diligence before making investment decisions. Past performance is not indicative of future results.
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Kirsten Co, MS, MBA, is the CEO of K&Company, where she works with AI startups to land and retain enterprise customers. With 15 years across enterprise sales, business development, and operations in the US, Asia Pacific, and Europe, and a Master’s in Global Security and Cybercrime from NYU, she contributes to Insider Monkey covering enterprise AI adoption, go-to-market strategy, and private AI companies worth watching for investors.
