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

1. NDNicolas Dessaigne0:00

   [upbeat music] Today I'm joined by Eric and Ulrik, the co-founders of Encord. They just announced a 60 million Series C led by Wellington Management. Congrats guys, and welcome to YC.

2. UWUlrik Waage0:16

   Thank you, Nicolas.

3. ELEric Landau0:16

   Thank you. Yeah.

4. NDNicolas Dessaigne0:17

   So let's jump in. What is Encord? Can you tell us, uh, what you are doing today?

5. UWUlrik Waage0:21

   Encord is AI-native data infrastructure, and, uh, we work with the world's top AI teams that are building different types of applications, predominantly in physical AI and robotics.

6. NDNicolas Dessaigne0:30

   So, so what is a data infrastructure? Like are you doing the labeling? Are you selling the data?

7. UWUlrik Waage0:35

   Ultimately, when companies build models, they need to make sure that the, the data going into the model is the right data, so getting the right data in, keeping the wrong data out, and we build, uh, our platform, a universal data layer for physical AI to effectively create, manage, annotate, and evaluate the data.

8. NDNicolas Dessaigne0:51

   And so what problem are you exactly solving for them?

9. UWUlrik Waage0:54

   So ultimately, a model is only as good as the data it's trained on, and even like the slightest errors in the data set can influence and impact like how the model actually works in the real world, and that is an incredibly difficult problem to solve because ultimately like the, uh, data sets only really get larger as, uh-

10. NDNicolas Dessaigne1:11

    Mm

11. UWUlrik Waage1:11

    ... AI complexity and, uh, size of the model scales. Once the model's in production, they have to make sure that they continue to feed it with the correct data that k- keeps pushing the frontiers of the, of, of the model.

12. NDNicolas Dessaigne1:22

    All right. So before to explore more what Encord is today, let's go back to the, the founding of the company. Can you tell us more about how you came up with the idea, the founding story?

13. UWUlrik Waage1:31

    So it was like right when AI was like starting to take off for the people that were, uh, taking note.

14. NDNicolas Dessaigne1:36

    Yeah.

15. UWUlrik Waage1:37

    So towards the end of the, the 2010s, um, I was doing a computer science masters at Imperial doing deep learning research. Eric was working at a high-frequency trading firm. I saw that out of the three ingredients for AI development, models, compute, and data, uh, the one that, uh, took the longest, we spent a bunch of time wrangling the data, spent a bunch of time cleaning the data, felt like the most, uh, defensible. Um, and so Eric had worked on big data systems and put thousands of models into production, and, uh, we saw that the way that AI development was being done was, again, sending all this data to-

16. NDNicolas Dessaigne2:10

    Mm

17. UWUlrik Waage2:10

    ... uh, the Philippines, getting it back, and we thought there had to be a better way. And so we, uh, decided to found Encord to solve, broadly speaking, the, the data problem in, in AI.

18. NDNicolas Dessaigne2:19

    And that was all before ChatGPT, so really you kind of like, uh, anticipated. You were already seeing the future in a way.

19. UWUlrik Waage2:26

    That's right.

20. NDNicolas Dessaigne2:27

    Is that the right way to frame it?

21. ELEric Landau2:28

    Uh, yeah, it was, um, it was definitely not the hot category when we were in YC.

22. NDNicolas Dessaigne2:33

    Like-

23. ELEric Landau2:33

    So-

24. NDNicolas Dessaigne2:33

    Like what, uh, what did you believe early like that, that was, uh [sniffs] , that looked unsexy then but now is obvious?

25. ELEric Landau2:41

    Uh, well, actually AI itself. Um, so in, in our batch, um, the, the main companies that were getting a lot of attention were fintech companies, they were crypto companies.

26. NDNicolas Dessaigne2:51

    A lot of crypto back then, right?

27. ELEric Landau2:52

    Yeah. Remote working was a huge one. Um, so we actually struggled to, to raise a seed round, and, uh, one of the funds that we get- got in like really late stages with-

28. NDNicolas Dessaigne3:00

    Mm-hmm

29. ELEric Landau3:00

    ... um, they e- ended up rejecting us 'cause they said the market wasn't gonna be big enough.

30. UWUlrik Waage3:04

    [laughs] Yeah.

Episode duration: 18:43