Encord & Scale AI Target Physical AI Data Growth in 2026

LONDON, February 26, 2026 — Encord, a San Francisco-based physical AI data infrastructure company, has secured $60 million in Series C funding to challenge Scale AI in multimodal data management. As adoption of self-driving cars, robotics, and drones accelerates, Encord aims to address inefficiencies in processing sensor-rich data like video, LiDAR, and telemetry, a critical step for advancing physical AI development. The funding, led by Wellington Management, values the company at $550 million.

Executive Summary

• Encord raised $60 million in Series C funding led by Wellington Management, reaching a $550 million valuation.
• The company aims to streamline multimodal data management for physical AI applications like drones, robotics, and autonomous vehicles.
• Clients include Woven by Toyota, Zipline, Skydio, and AXA Financial, with revenue from physical AI customers increasing tenfold in the past year.
• Encord differentiates itself with an AI-native data architecture, addressing a key bottleneck in AI development.

Key Developments

The shift of physical AI from experimental labs to commercial application in industries such as automotive and logistics has presented new challenges for managing complex, multimodal data. Encord’s recent $60 million Series C funding round, led by Wellington Management with participation from Y Combinator, CRV, N47, Crane Venture Partners, Harpoon Ventures, Bright Pixel Capital, and Isomer Capital, highlights the growing importance of robust data infrastructure. With this round, Encord’s total funding has reached $110 million.

Founded by Ulrik Stig Hansen and Eric Landau, Encord is designed to handle the full data lifecycle for physical AI, including capturing, organizing, annotating, and aligning data for retraining. The company’s commitment to automation and active learning sets it apart from competitors like Labelbox and Scale AI. Its platform has scaled rapidly, now managing over 5 petabytes of data, a fivefold increase from the previous year.

Encord’s focus on AI-native architecture allows it to excel in handling sensor-heavy, multimodal data types such as video, LiDAR, and 3D sensor data. Hansen notes, “The data flywheel and feedback loop is the core differentiator, enabling continuous learning and improved data quality.”

Market Context

As industries like automotive, robotics, and logistics increasingly integrate AI systems, the demand for efficient data infrastructure has surged. Unlike large language models trained on vast textual datasets, physical AI relies on sensor-driven, real-world data, which is far more complex to manage. This has created a bottleneck, as existing tools often fall short in terms of automation and scaling capabilities. Companies like Scale AI have traditionally dominated this space, but Encord’s entry signals a shift toward more specialized, AI-native solutions.

The global AI infrastructure market is expected to exceed $100 billion by 2030, driven by advancements in robotics, autonomous vehicles, and drones. Encord’s approach to unifying data workflows aligns with industry needs for streamlined processes and reduced fragmentation. This positions the company well in a rapidly evolving market.

BUSINESS 2.0 Analysis

Encord’s latest funding round underscores the growing importance of data infrastructure in physical AI. While much of the AI industry’s focus has historically been on model development, Encord is tackling the foundational issue of data management. This approach not only addresses inefficiencies but also enables faster innovation cycles in industries dependent on physical AI.

The company’s focus on automation and active learning is particularly noteworthy. By integrating human-in-the-loop systems with continuous learning mechanisms, Encord enhances data quality over time, a critical factor for applications in self-driving cars and drones, where data is constantly evolving. This positions Encord as a key player in the physical AI ecosystem, providing a universal data layer that could become indispensable for companies looking to scale their AI operations.

However, challenges remain. Competing against established players like Scale AI will require not just technological superiority but also aggressive market penetration strategies. Additionally, as the company scales globally, maintaining diversity and operational efficiency will be critical. With over 40 nationalities represented among its 150+ employees, Encord has laid a strong foundation for global expansion.

Why This Matters for Industry Stakeholders

For automotive and robotics companies, Encord’s platform offers a way to streamline data management, reducing the time spent on data curation and annotation. This could accelerate development timelines and improve the quality of AI models, giving companies a competitive edge. Investors should note the tenfold revenue growth from physical AI customers, indicating strong demand for Encord’s solutions.

For AI developers, the emphasis on automation and active learning could significantly reduce the manual effort involved in data preparation. This not only lowers costs but also enables more frequent model retraining, a critical factor in dynamic environments like autonomous driving.

Forward Outlook

With $60 million in fresh funding, Encord is well-positioned to expand its product offerings and enter new markets. The company’s focus on automation and AI-native architecture aligns with industry trends, making it a strong contender in the physical AI data infrastructure space. However, scaling globally will require strategic partnerships and continued innovation to stay ahead of competitors like Scale AI.

Given the rapid adoption of physical AI technologies, Encord’s growth trajectory appears promising. The next few years will be crucial in determining whether it can establish itself as the go-to platform for multimodal data management.

Key Takeaways

• Encord raised $60 million in Series C funding, reaching a $550 million valuation.
• The platform aims to streamline multimodal data management for physical AI.
• Key differentiators include automation and continuous learning capabilities.
• Clients include Woven by Toyota, Zipline, and Skydio.
• Global expansion and scaling are next on the agenda.

References

1. Source: TechFundingNews
2. Bloomberg
3. Financial Times
4. More AI Coverage
5. Funding News