As AI companies scramble to build ever more capable models, data labeling has gotten its moment in the spotlight. Ex. the Meta-Scale AI deal, which saw Meta take a 49% stake in Scale and hire its CEO, injected new energy and scrutiny. With foundational models shifting from static, pre-trained systems to continually evolving, reinforcement-tuned AI agents, the role of high-quality, domain-specific data is more important than ever.
Labelbox wants to lead this shift, even as advances in synthetic training models raise existential questions about the future of human-in-the-loop data labeling. Co-founder and CEO Manu Sharma appeared on a16z’s podcast to talk about what he sees as their opportunities.
AI Created an Inflection Point
Over time, as transformers and generative models to
Labelbox Bets on Experts Even as AI Leans Into Self-Training
- By Mukundan Sivaraj
- Published on
"Nearly every dataset... requires a fusion of AI software and humans. There is just no way to produce the best data in isolation.”
