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In focus
- Why Datasaur chose private AI over Scale’s data labeling leftovers
- A visual look at SEA founders chasing the American dream
- A system glitch that blew up Grab fares in Malaysia and Singapore
Hello reader,
In late 2022, Indonesian ecommerce sellers rushed to cash in on lato-lato, a traditional toy that went so viral it even ended up in the hands of Singapore’s then-deputy prime minister Lawrence Wong.
Most merchants stuck with the trend, fighting for scraps from the craze. A few, though, took it as a signal to branch out and stock other toys, betting that the real opportunity was in riding TikTok-driven demand more broadly.
Something similar is happening with AI. When Meta acquihired Scale AI in June, many of Scale’s customers grew uneasy about sending proprietary data to a company now part of Meta’s empire. They began looking for other labeling vendors.
Scale’s rivals have been busy scooping up those contracts. But Datasaur, a US startup founded by American-born Indonesian Ivan Lee, saw a different opening. Instead of doubling down on labeling, it shifted focus to private large language model (LLM) deployments for enterprises.
In today’s Big Story, I look at how Datasaur is navigating this shift – and why it makes sense for a labeling player to bank on private AI.
In a space as volatile as AI, hopping on the trend may deliver quick wins. But the real game is finding a durable market that could also bring you closer to profits.
For more of our AI coverage, head here.
Glenn Kaonang, journalist
The Big Story
Why Datasaur is chasing private AI over scraps in data labeling
Image credit: Datasaur
After six years in labeling for natural language processing, Datasaur is now busy delivering customized AI models for companies in regulated industries. The startup believes its expertise carries over well, helping enterprises clean and structure data before putting it into their AI systems.
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