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Robots Don't Need More Compute. They Need This.

Encord is building the AI-native data infrastructure for physical AI and robotics, helping the world's top AI teams create, manage, annotate, and evaluate the data that goes into their models. The company just announced a $60 million Series C led by Wellington Management. In this episode of Founder Firesides, co-founders Eric and Ulrik sat down with YC's Nicolas Dessaigne to talk about building a data infrastructure company before ChatGPT made AI a hot category, why physical AI is the next frontier but needs a fundamentally different approach to data than LLMs, and how they're opening an R&D facility in the Bay Area where robotics companies can bring their own hardware and collect the training data they need to get to market.

NDNicolas DessaignehostUWUlrik WaageguestELEric Landauguest

Apr 30, 2026·18m·Watch on YouTube ↗

📖 CHAPTERS

1. 1

   Encord’s mission: a universal data layer for physical AI

   Ulrik explains Encord as AI-native data infrastructure focused on physical AI and robotics teams. The company’s core aim is to ensure the right data goes into models—created, curated, annotated, and evaluated in one platform.

2. 2

   Why data becomes the bottleneck as models scale

   The conversation frames model performance as constrained by data quality and data operations complexity. As models and deployments scale, datasets grow and continuous data feeding becomes essential to push performance in production.

3. 3

   Founding story: spotting the defensible wedge in AI (pre-ChatGPT)

   Ulrik and Eric describe meeting as AI was taking off in the late 2010s and realizing data wrangling consumed the most time and could be highly defensible. They saw a broken workflow—outsourcing labeling overseas—and believed software could do better.

4. 4

   Early market skepticism: building AI for teams that didn’t trust AI

   Eric notes AI/data tooling wasn’t a hot category in their YC era, when crypto/fintech drew more attention. They even struggled to raise seed because some investors doubted the AI market size, reflecting how early the bet was.

5. 5

   Scale today: customers, team footprint, and Series C milestone

   Ulrik shares Encord’s current scale: hundreds of AI teams across autonomous driving and robotics, with a sizable team split between London and San Francisco. The announcement includes a $60M Series C and $110M raised total.

6. 6

   The first product: automating computer-vision annotation workflows

   They describe Encord’s early product as annotation automation for computer vision—improving a process that was slow and operationally heavy. The initial target was image segmentation and related CV labeling workflows.

7. 7

   ChatGPT changed trust: from skepticism to acceptance of automation

   Eric explains how ChatGPT was a turning point because it normalized trusting AI systems, even among AI companies. Encord had developed “micro models” to assist labeling, but customers were hesitant until the broader market saw AI work reliably in general settings.

8. 8

   Shift to multimodal and physical AI: why the data problem flips

   After ChatGPT, Encord leaned into multimodal data (video, audio, text, sensors) and physical AI. Eric contrasts digital AI—where internet text made data plentiful—with physical AI, where compute exists but real-world embodied data is scarce.

9. 9

   New offering: enabling real-world data collection and pre-training support

   Ulrik introduces a new initiative: an R&D facility in the Bay Area to help robotics companies collect embodied data for pre-training and training. Encord doesn’t build robots; it provides environments and the data pipeline to capture and operationalize training data.

10. 10

    Post-deployment needs: exception handling, observability, and QA in the real world

    They argue the next phase for physical AI is operating safely in production, where failures have real consequences. Encord aims to support post-deployment workflows like exception handling and observability, linking real-world events back to model improvement.

11. 11

    Humans-in-the-loop remain essential because stakes and frontier tasks are different

    They explain why human oversight persists in physical AI: frontier tasks and safety-critical contexts demand high-quality supervision. Humans also act as managers/supervisors of AI systems, especially when tolerance for errors is low.

12. 12

    What customers buy: faster path to market and better model performance

    Encord is positioned as infrastructure that lets robotics and autonomy teams focus on building products rather than building data stacks. The value proposition centers on accelerating iteration and improving model quality via unified data workflows.

13. 13

    Example in production: Weave Robotics and the laundry-folding robot

    Ulrik highlights Weave Robotics (a YC company) as a customer bringing a laundry-folding robot to market. The example illustrates Encord’s role as the physical AI data platform behind real consumer-facing robotics deployments.

14. 14

    Series C rationale and the outlook for robots: hype, consolidation, then scale

    Eric explains the fundraise as an acceleration play as physical AI attention and investment increase. They predict a trajectory similar to self-driving cars: hype, consolidation, then rapid progress toward general-purpose household robots over a few years.

15. 15

    Ambition, hiring (including agents), and founder lessons on decision-making

    They share Encord’s long-term ambition: become the default system through which physical AI data flows, analogous to Stripe for payments. The discussion also covers hiring across SF/London, adding internal AI agents, and advice on speed of decisions and adapting tactics while keeping long-term direction.