In this episode of AI + a16z, Labelbox CEO Manu Sharma joins a16z Infra partner Matt Bornstein to explore the evolution of data labeling and evaluation in AI — from early supervised learning to today’s sophisticated reinforcement learning loops.

Manu recounts Labelbox’s origins in computer vision, and how the shift to foundation models and generative AI changed the game. The value moved from pre-training to post-training and, today, models are trained not just to answer questions, but to assess the quality of their own responses. Labelbox has responded by building a global network of experts — top professionals from fields like  coding, healthcare, and customer service, who label and evaluate data used to fine-tune AI systems and align outputs with users’ expectations.

The conversation also touches on Meta’s acquisition of Scale AI, underscoring how critical data and talent have become in the AGI race.

Here’s a sample of Manu explaining how Labelbox was able to transition from one era of AI to another:

It took us some time to really understand like that the world is shifting from building AI models to renting AI intelligence. A vast number of enterprises around the world are no longer building their own models; they’re actually renting base intelligence and adding on top of it to make that work for their company. And that was a very big shift. 

But then the even bigger opportunity was the hyperscalers and the AI labs that are spending billions of dollars of capital developing these models and data sets. We really ought to go and figure out and innovate for them. For us, it was a big shift from the DNA perspective because Labelbox was built with a hardcore software-tools mindset. Our go-to market, engineering, and product and design teams operated like software companies. 

But I think the hardest part for many of us, at that time, was to just make the decision that we’re going just go try it and do it. And nothing is better than that: “Let’s just go build an MVP and see what happens.”