Wirestock raises $23M to supply creative multimodal data to AI labs

2026-05-14 · Source: AI News & Artificial Intelligence | TechCrunch · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Emerging Technologies & Innovation · Depth: Fundamental Awareness, short

Summary

Wirestock, a platform that initially helped photographers distribute and sell their work on stock photography services, pivoted in 2023 to become an AI data provider. The company now supplies datasets of images, videos, design assets, and gaming and 3D content to AI labs, having signed up over 700,000 artists and designers. Wirestock announced it has raised $23 million in Series A funding, led by Nava Ventures, to expand this new data supply business, bringing its total capital raised to approximately $26 million. The startup currently serves six major foundation model makers, boasts an annual run-rate revenue of $40 million, and has paid out $15 million to its contributors. It focuses on multimodal data for creative AI applications and plans to use the funding to hire for research, engineering, and product roles, and to build enterprise software for AI labs.

Key takeaway

For CTOs and VPs of Engineering evaluating data procurement strategies for foundation models, Wirestock's pivot highlights a viable model for acquiring diverse, multimodal training data. You should consider engaging specialized data providers that can source custom content and manage contributor networks, rather than solely relying on public datasets, to meet the increasing demand for high-quality, domain-specific AI training data.

Key insights

Creative marketplaces are pivoting to become AI data providers, leveraging existing content libraries and artist networks.

Principles

• Transparency with contributors is crucial during business model shifts.
• Custom content requests drive new opportunities for creators.

Method

Wirestock acquires contributors via email marketing and referrals, requiring an unpaid quality check task, and uses a mix of AI and human reviews for content evaluation.

In practice

• Explore licensing existing content libraries to AI labs.
• Develop custom content request workflows for AI training data.

Topics

• AI Data Supply
• Multimodal Datasets
• Series A Funding
• Foundation Models
• Creative AI Applications

Best for: CTO, VP of Engineering/Data, Investor, Entrepreneur, Director of AI/ML

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Editorial summary, takeaway, and curation by AIssential. Original article published by AI News & Artificial Intelligence | TechCrunch.