Synthetic data is moving from “nice-to-have” to default infrastructure for AI development, pushed by privacy constraints and data scarcity. The tradeoff is now clear: scaling synthetic data without degrading model quality requires stronger governance, measurement, and transparency.
Synthetic Test Data for Artificial Intelligence (AI) Research Report 2025: $8.24 Bn Market Opportunities, Trends, Competitive Analysis, Strategies and Forecasts 2019-2024, 2024-2029F, 2034F
GlobeNewswire highlighted a market research report forecasting rapid growth in the synthetic test data for AI market, projecting expansion from $1.81 billion in 2024 to $2.46 billion in 2025—a 35.7% CAGR. The report frames synthetic test data as a fast-growing layer of AI delivery: used to validate models, unblock development when real data is constrained, and reduce exposure to sensitive personal data.
The report also flags what buyers are asking for: broader adoption of AI-generated synthetic data, more privacy-preserving generation techniques, and synthetic data products explicitly designed to be regulatory-compliant. In other words, the market pull is not just “more data,” but auditable and policy-aligned data that can stand up to internal governance and external scrutiny.
- Budget signal: Growth projections suggest synthetic data is becoming a planned line item for AI programs (testing, validation, and data access), not an experimental workaround.
- Compliance as a product requirement: Demand is shifting toward privacy-preserving and regulatory-compliant synthetic data, pushing vendors to provide controls, documentation, and governance hooks.
- Operational implication: Data teams should expect rising expectations for repeatable pipelines (generation → evaluation → approval) rather than ad hoc dataset creation.
Tech companies are turning to 'synthetic data' to train AI models
TechXplore reports that more tech companies are leaning on synthetic data to train AI models as real-world data becomes harder to obtain at the needed scale—whether due to scarcity, access restrictions, or diminishing returns from existing corpora. The piece positions synthetic data as a pragmatic response to “running out” of fresh training material, with the appeal of faster iteration and fewer direct privacy entanglements.
But the article also emphasizes failure modes: over-reliance on synthetic data can contribute to model collapse and increased hallucinations if synthetic content is fed back into training loops without controls. The practical takeaway is that synthetic data is not automatically “safe” or “high quality” just because it’s not directly sourced from individuals; teams still need strong quality management, disclosure, and hybrid strategies that keep models grounded.
- Quality risk becomes a governance risk: If synthetic data degrades performance (collapse/hallucinations), it can undermine reliability claims and create downstream compliance and safety exposure.
- Hybrid is the new default: Many teams will need mixed pipelines (real + synthetic) with explicit rules for when synthetic data is allowed and how it’s validated.
- Transparency pressure: Expect increasing internal demands (risk, legal, audit) to document provenance, generation methods, and evaluation results for synthetic training datasets.