Scale AI revenue doubles to $1.6B annualized — Meta's data-labeling and RLHF spend the dominant driver, IPO timing accelerated to 2027
Scale AI confirmed on May 28 that revenue has doubled to a $1.6B annualized run-rate, with Meta's data-labeling and RLHF spend the dominant single-customer driver. The company's IPO timing — previously targeted for late 2027 — has been accelerated to mid-2027 in response to the revenue trajectory and the broader AI-infrastructure-IPO window. The Meta relationship — already a major component of Scale's mix through 2025 — has intensified following the open-source Llama 4 cadence.
The revenue trajectory is the substantive piece. Scale's reported revenue passed $1B annualized in early 2026; the May 28 update doubles that to $1.6B with year-over-year growth accelerating rather than decelerating. The customer-concentration distribution has shifted: Meta is now approximately 30% of revenue (up from ~18% a year ago), the major frontier labs (OpenAI, Anthropic, Google DeepMind) collectively account for roughly 40%, and the long tail of enterprise and government customers makes up the remaining 30%. The data-labeling and RLHF spend per major customer has accelerated as model post-training has shifted from short-cycle fine-tunes to multi-month, multi-modal alignment pipelines.
The IPO timing is the strategic-signal piece. Meta's Llama 4.1 70B Thinking release the same day underscores the open-source cadence that is driving Scale's revenue mix shift. The acceleration to mid-2027 reflects three convergent factors: revenue scale has hit the threshold public markets reward, the broader AI-infrastructure-IPO window opened by Anthropic's ARR trajectory and the various data-center-and-power infrastructure IPOs is open through the back half of 2026 and into 2027, and Scale's customer-concentration risk is structurally bounded by the multi-lab nature of the post-training-data market. For investors, the public-market entry of Scale at the operational scale the company now occupies is the most credible test of the long-thesis that data-labeling-as-infrastructure is a durable business model.
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